Method and device to monitor patients with kidney disease

ABSTRACT

A medical monitoring device for monitoring electrical signals from the body of a subject is described. The medical monitoring device monitors electrical signals originating from a cardiac cycle of the subject and associates each cardiac cycle with a time index. The medical monitoring device applies a forward computational procedure to generate a risk score indicative of hyperkalemia, hypokalemia or arrhythmia of the subject. The medical monitoring device can adjust the forward computational procedure based upon clinical data obtained from the subject.

FIELD OF THE INVENTION

The invention relates to an electronic medical device for monitoring amammal with kidney disease and issuing alerts if a kidney diseasecondition of the subject worsens. The systems and methods of theinvention include an electronic circuit, sensors, a computer processor,a computational procedure and telecommunication means. The inventionfurther relates to methods for signal processing and parameteridentification.

BACKGROUND

Dialysis simulates kidney function by periodically removing wastesolutes and excess fluid such as urea and ions from a patient's blood.This is accomplished by allowing the body fluids, usually blood, to comeinto close proximity with a dialysate, which is a fluid that serves tocleanse the blood and that actively removes the waste products includingsalts and urea, and excess water. Each dialysis session lasts a fewhours and may typically be repeated as often as three times a week ormore, such as 7 days a week.

Although effective at removing wastes from blood, dialysis treatmentsperformed at dialysis centers are administered intermittently andtherefore fail to replicate the continuous waste removal aspect of anatural and functioning kidney. Once a dialysis session is completed,fluid and other substances such as the sodium and potassium saltsimmediately begin to accumulate again in the tissues of the patient.Notwithstanding the benefits of dialysis, statistics indicate that threeout of five dialysis patients die within five years of commencingtreatment. Studies have shown that increasing the frequency and durationof dialysis sessions can improve the survivability of dialysis patients.Increasing the frequency and duration of dialysis sessions more closelyresembles continuous kidney function. However, the requirement forpatients to travel to the dialysis centers and the costs associated withthe hemodialysis procedure itself pose an upper limit on the frequencyof dialysis procedures.

Another complication is that as blood potassium levels increase betweendialysis sessions, patients become more susceptible to life threateningarrhythmias. Similarly, low concentration of potassium can be dangerousby causing muscle weakness. Significant deviations from a normalphysiological range of potassium must be detected and prevented to avoidworsening of patient conditions. In particular, patients with kidneydisease (KD) are not able to adequately regulate bodily fluid levels andcommon blood solutes such as potassium ion. As such, KD patients are atrisk for developing hyperkalemia (high blood potassium concentration) orhypokalemia (low blood potassium concentration). Normal blood potassiumlevel is from 3.5 to 5.0 mEq; however, KD patients may tend to falloutside this range between treatments. Hyperkalemia and hypokalemia canlead to heart palpitations and arrhythmias.

Since patients with kidney failure cannot effectively eliminatepotassium from their bodies, potassium must be removed duringhemodialysis sessions. Between dialysis sessions of hyperkalemicpatients, serum potassium concentration increases gradually until thenext dialysis session. This increase in the potassium concentrations isa major cause of the increased rate of cardiovascular complications thatis observed in the patients with kidney disease. Approximately 30% ofthese patients have atrial fibrillation, and according to the 2003-2005USRDS data, an additional 6.2% deaths/year are caused by cardiac arrestsor arrhythmias (“Primer on Kidney Diseases”, 5th Ed., A. Greenberg etal., pp 504-5). Hence, there is a clear unmet need for monitoringpatients between dialysis sessions. There is also an unmet need formonitoring and managing hyperkalemia, hypokalemia or arrhythmias inpatients with KD.

In addition to being in danger of exposure to the complications ofabnormal potassium levels between dialysis sessions, many kidneypatients also experience an extreme variation of potassium levels duringtheir dialysis sessions that increases their health risk. Duringhemodialysis, there is a net addition of base in the form ofbicarbonate, which increases the cellular uptake of potassium andattenuates the overall removal of potassium from the cells. Hence,patients may initially experience an increase in their intracellularpotassium levels followed by a reduction in levels resulting inhypokalemia. This condition is of particular concern to patients withunderlying cardiac conditions. As such, there is a clear unmet need toguard against risk to patients during the dialysis sessions and duringthe post-treatment period.

SUMMARY OF THE INVENTION

The present invention in one or more embodiments provides a medicalsystem for monitoring serum potassium concentration in a subject, themedical system including a medical device, a processor and acommunication device, wherein the medical device includes at least oneof an electromyogram sensor and an electrocardiogram sensor fordetecting a change in muscle or nerve activity of a subject and forproducing at least one output electrical signal based on the change inmuscle or nerve activity as detected by at least one received electricalsignal, the output electrical signal being indicative of a serumpotassium concentration of the subject, wherein the processor applies aforward computational procedure to the at least one received electricalsignal to generate a risk score for hyperkalemia, hypokalemia orarrhythmia, and wherein the communication device indicates a conditionof hyperkalemia, hypokalemia or arrhythmia of the subject based on therisk score.

In certain embodiments, the medical device may be provided alone and themedical system does not necessarily have to include the processor or thecommunication device.

In certain embodiments, the medical system for monitoring serumpotassium concentration in the subject may further include a pulsegenerator for producing one or more pulse sets, and one or moredetectors or pulse-sensing electrodes for mediating communicationbetween the pulse generator and the subject, such that the change ofmuscle or nerve activity in the subject is initiated by the one or morepulse sets and mediated by the one or more detectors or pulse-sensingelectrodes.

In certain embodiments, the medical device as employed in the medicalsystem for monitoring serum potassium concentration in the subject andreceiving at least one electrical signal may be externally applicable tothe subject or is implantable in the subject.

The one or more detectors may include at least one of a nerveelectrogram amplifier, an accelerometer, a strain gauge and a pressuregauge for detecting the change in muscle or nerve activity.

In certain embodiments, the pulse generator as employed in the medicalsystem for monitoring serum potassium concentration in the subject maybe provided with an pulsing schedule such that the one or more pulsesets include a first pulse set and a second pulse set, the first andsecond pulse sets are produced according to one or more of the followingpulsing rules: i) the first and second pulse sets are separated in timeby 0.5 to 5 seconds; ii) the first and second pulse sets eachindependently include 3 to 10 pulses; and iii) the first and secondpulse sets each independently having a frequency of 2 to 50 Hz.

In certain embodiments, the pulsing schedule of the pulse generator asemployed in the medical system for monitoring serum potassiumconcentration in the subject may be provided such that the first andsecond pulse sets are produced according to two or more of the followingpulsing rules: i) the first and second pulse sets are separated in timeby 1.5 to 2.5 seconds; ii) the first and second pulse sets eachindependently include 4 to 6 pulses; and iii) the first and second pulsesets each independently having a frequency of 2 to 10 Hz.

In certain embodiments, the medical system for monitoring serumpotassium concentration in the subject may further include an amplifierfor mediating communication between the pulse generator and the one ormore electrodes.

In certain embodiments, the electromyogram sensor as employed in themedical system for monitoring serum potassium concentration in thesubject may be a skeletal muscle strain sensor or a blood pressuresensor.

In certain embodiments, the electrocardiogram sensor as employed in themedical system for monitoring serum potassium concentration in thesubject may include one or more electrocardiogram electrodes forreceiving one or more electrocardiogram features from the subject, theone or more electrocardiogram features including at least one offeatures F1 through F16 tabulated in Table 1 below, and in certainparticular instances, the one or more electrocardiogram featuresincluding T-wave amplitude, R-wave amplitude, T-slope, ratio of T-waveamplitude to R-wave amplitude (T/R ratio), and T-wave flatness.

In certain embodiments, the medical system for monitoring serumpotassium concentration in the subject may further include anelectrocardiogram algorithm for producing an output on serum potassiumconcentration in the subject based on a value of the one or moreelectrocardiogram features, wherein the electrocardiogram algorithmincludes one or more of the following operational rules: i) the outputon the serum potassium concentration being a function of the R-waveamplitude; ii) the output on the serum potassium concentration being afunction of the T-wave amplitude; iii) the output on the serum potassiumconcentration being a function of the T/R ratio; and iv) the output onthe serum potassium concentration being a function of the T-waveflatness.

In certain embodiments, the output on the serum potassium concentrationproduced according to the electrocardiogram algorithm may be adifference between a serum potassium concentration at time t₁ of thesubject and a baseline potassium concentration at time t₀ of thesubject, time t₁ being at least 10 minutes apart from t₀.

In certain embodiments, the baseline serum potassium concentration maybe a value selected from the group consisting of a baseline serumpotassium concentration of the subject obtained at a periodic blooddraw, a baseline serum potassium concentration of the subject obtainedat the onset of a dialysis session, and a baseline serum potassiumconcentration of the subject at the end of a dialysis session.

In certain embodiments, the R-wave amplitude of operational rule i) maybe a difference between an R-wave amplitude at time t₁ of the subjectand a baseline R-wave amplitude at time t₀ of the subject, the T-waveamplitude of operational rule ii) may be a difference between a T-waveamplitude at time t₁ of the subject and a baseline T-wave amplitude attime t₀ of the subject, the T/R ratio of operational rule iii) may be adifference between a T/R ratio at time t₁ of the subject and a baselineT/R ratio at time t₀ of the subject, and the T-wave flatness ofoperational rule iv) may be a difference between an T-wave flatness attime t₁ of the subject and a baseline T-wave flatness at time t₀ of thesubject.

In certain embodiments, the baseline potassium concentration of thesubject may be 3.0 to 5.5 mM at time t₀.

In certain embodiments, the one or more electrocardiogram electrodes mayinclude one or more of lead II, lead V2, lead V3 and lead V4.

In certain embodiments, the one or more electrocardiogram electrodes mayconsist only of lead II.

In certain embodiments, the electrocardiogram algorithm may include oneor more of the operational rules i), iii) and iv).

In certain embodiments, the output on the serum potassium concentrationproduced according to the electrocardiogram algorithm may be in positivecorrelation with the R-wave amplitude.

In certain embodiments, the output on the serum potassium concentrationproduced according to the electrocardiogram algorithm may be is innegative correlation with the T-wave amplitude.

In certain embodiments, the output on the serum potassium concentrationproduced according to the electrocardiogram algorithm may be in negativecorrelation with the T-slope.

In certain embodiments, the output on the serum potassium concentrationproduced according to the electrocardiogram algorithm may be in positivecorrelation with the T-wave flatness.

In certain embodiments, the one or more electrocardiogram electrodes asemployed in the medical system for monitoring serum potassiumconcentration in the subject may include a first set of electrodesconsisting of lead II, lead V2, lead V3 and lead V4, and a second set ofelectrodes consisting of lead II only, wherein the electrocardiogramalgorithm further includes a calibration rule that the medical system isoperationally calibrated if one or more of the following is met: v) adifference between the outputs according to rule ii) from the first setof electrodes and the second set of electrodes is no greater than 20%;vi) a difference between the outputs according to rule iii) from thefirst set of electrodes and the second set of electrodes is no greaterthan 20%; and vii) a difference between the outputs according to ruleiv) from the first set of electrodes and the second set of electrodes isno greater than 20%.

In certain embodiments, the one or more electrocardiogram electrodes asemployed in the medical system for monitoring serum potassiumconcentration in the subject may include a first set of electrodesconsisting of lead II, lead V2, lead V3 and lead V4, and a second set ofelectrodes consisting of lead II only, wherein the electrocardiogramalgorithm further includes a calibration rule that the medical system isoperationally calibrated if one or more of the following is met: v) adifference between the outputs according to rule ii) from the first setof electrodes and the second set of electrodes is no greater than 10%;vi) a difference between the outputs according to rule iii) from thefirst set of electrodes and the second set of electrodes is no greaterthan 10%; and vii) a difference between the outputs according to ruleiv) from the first set of electrodes and the second set of electrodes isno greater than 10%.

In certain embodiments, the medical system for monitoring serumpotassium concentration in the subject may further include a dialysisdevice such that the subject is under serum potassium concentrationmonitoring while being subject to a dialysis treatment by the dialysisdevice.

In certain embodiments, the forward computational procedure as employedin the medical system for monitoring serum potassium concentration inthe subject may provide a non-weighted or weighted sum of the featurescores P1, P6, P7, P8 and P10 to calculate the risk score with thefeature scores each assigned a zero or non-zero value based on thefollowing: P1 being a feature score based on P-R interval, P6 being afeature score based on S-T segment depression, P7 being a feature scorebased on T-wave inversion, P8 being a feature score based on U-waveamplitude, and P10 being a feature score based on heart rate variation.

In certain embodiments, the forward computational procedure as employedin the medical system for monitoring serum potassium concentration inthe subject may provide a non-weighted or weighted sum of the featurescores P2, P3, P4, P5, P9 and P10 each assigned a zero or non-zero valuebased on the following: P2 being a feature score based on QRS width, P3being a feature score based on Q-T interval, P4 being a feature scorebased on P-wave amplitude, P5 being a feature score based on P-wavepeak, P9 being a feature score based on T-wave amplitude, and P10 beinga feature score based on heart rate variation.

In certain embodiments, the forward computational procedure as employedin the medical system for monitoring serum potassium concentration inthe subject may provide a non-weighted or weighted sum of the featurescores P1 through P16 each assigned a zero or non-zero value based onthe following: P1 being a feature score based on P-R interval, P2 beinga feature score based on QRS width, P3 being a feature score based onQ-T interval, P4 being a feature score based on P-wave amplitude, P5being a feature score based on P-wave peak, P6 being a feature scorebased on S-T segment depression, P7 being a feature score based onT-wave inversion, P8 being a feature score based on U-wave amplitude, P9being a feature score based on T-wave amplitude, P10 being a featurescore based on heart rate variation, P11 being a feature score based onratio of T-wave amplitude to R-wave amplitude, P12 being a feature scorebased on T-wave flatness, P13 being a feature score based on T-slope,P14 being a feature score based on T-wave peak to T-wave end (TpkTend),P15 being a feature score based on QT/TpkTend, and P16 being a featurescore based on T-wave phase type.

The present invention in one or more embodiments further provides amethod for monitoring serum potassium concentration in a subject, themethod including the steps of applying to a subject one or more pulsesets generated by a pulse generator, connecting at least oneelectromyogram sensor to the subject to receive at least one electricalsignal from the subject in response to the one or more pulse sets,generating an output from the at least one electromyogram sensor inresponse to the at least one electrical signal, the output beingindicative of a level of serum potassium concentration of the subject,applying a forward computational procedure to the output to generate arisk score, and issuing an alert indicating a condition of hyperkalemia,hypokalemia or arrhythmia of the subject based on the risk score.

In certain embodiments, the method for monitoring serum potassiumconcentration in the subject may be carried out such that the one ormore pulse sets are generated according to a pulsing schedule such thatthe one or more pulse sets include a first pulse set and a second pulseset, the first and second pulse sets are produced according to one ormore of the following pulsing rules: i) the first and second pulse setsare separated in time by 0.5 to 5 seconds; ii) the first and secondpulse sets each independently include 3 to 10 pulses; and iii) the firstand second pulse sets each independently having a frequency of 2 to 50Hz.

In certain embodiments, the pulsing schedule of the pulse generatoremployed in the method for monitoring serum potassium concentration inthe subject may be provided such that the first and second pulse setsare produced according to two or more of the following pulsing rules: i)the first and second pulse sets are separated in time by 1.5 to 2.5seconds; ii) the first and second pulse sets each independently include4 to 6 pulses; and iii) the first and second pulse sets eachindependently having a frequency of 2 to 10 Hz.

The present invention in one or more embodiments further provides amethod for monitoring serum potassium concentration in a subject, themethod including: connecting at least one electrocardiogram sensor to asubject to receive one or more electrocardiogram features; applying anelectrocardiogram algorithm to the one or more electrocardiogramfeatures to obtain an indicator for serum potassium concentration of thesubject, wherein the electrocardiogram algorithm includes one or more ofthe following operational rules: i) the output on the serum potassiumconcentration being a function of the R-wave amplitude; ii) the outputon the serum potassium concentration being a function of the T-waveamplitude; iii) the output on the serum potassium concentration being afunction of the T/R ratio; and iv) the output on the serum potassiumconcentration being a function of the T-wave flatness; applying aforward computational procedure to the output to generate a risk score;and issuing an alert indicating a condition of hyperkalemia, hypokalemiaor arrhythmia of the subject based on the risk score.

In certain embodiments, a computer-readable medium has instructionsthat, when executed by a blood fluid removal device, cause the device to(i) initiate a blood fluid removal session with initial systemparameters; (ii) acquire a first set of data regarding one or morepatient physiological parameters; store the first data set in a mosteffective to date data set memory; (iii) associate the initial systemparameters in an increased effectiveness lookup table with the firstdata set; (iv) adjust at least one parameter of the blood fluid removalsession to arrive at adjusted system parameters; (v) acquire a secondset of data regarding the one or more patient physiological parametersafter the at least one parameter of the blood fluid removal session hasbeen adjusted; and (vi) if at least one value of the second data set iscloser to a target value than a corresponding at least one value of thefirst data set: replace the first data set in the most effective to datedata set memory with the second data set; store in the increasedeffectiveness lookup table data regarding the second data set; andassociate data regarding the adjusted system parameters with the seconddata set.

Other objects, features and advantages of the present invention willbecome apparent to those skilled in the art from the following detaileddescription. It is to be understood, however, that the detaileddescription and specific examples, while indicating some embodiments ofthe present invention are given by way of illustration and notlimitation. Many changes and modifications within the scope of thepresent invention may be made without departing from the spirit thereof,and the invention includes all such modifications.

BRIEF DESCRIPTION OF FIGURES

FIG. 1 is an exemplary embodiment of an EKG monitor.

FIG. 2 is an exemplary embodiment of an EKG monitor having additionalfunctionality to supply an electrical stimulation to muscle tissue and asensor to observe a mechanical response.

FIG. 3 is an illustrative mechanical response of muscle tissue toelectrical stimulation depending upon a potassium environment.

FIG. 4 is a graphical representation of discrete computationalprocedures to determine feature scores in accordance with someembodiments of the invention.

FIG. 5 is a graphical representation of continuous computationalprocedures to determine feature scores in accordance with someembodiments of the invention.

FIG. 6 is a flow chart of a process to issue an alert in accordance withsome embodiments.

FIG. 7 is shows a disease risk score trend.

FIG. 8 shows the application of a correction to minimize error inaccordance with some embodiments.

FIG. 9 shows a monitoring of a medical system or device in accordancewith some embodiments.

FIG. 10 shows an additional system for monitoring a medical device inaccordance with some embodiments.

FIG. 11 shows the acquisition of feature values for an ECG.

FIG. 12 shows a process for setting feature scores on a common scale inaccordance with some embodiments.

FIG. 13 shows a process for issuing an alert for hypokalemia orhyperkalemia in accordance with some embodiments.

FIGS. 14-18 show flow diagrams illustrating methods in accordance withcertain embodiments described herein.

FIGS. 19-25 show flow diagrams illustrating methods in accordance withcertain embodiments described herein.

FIG. 26 shows a schematic graphical representation of monitoredprophetic data shown for purposes of illustration.

FIGS. 27-28 each show a medical system in accordance with someembodiments;

FIG. 29 shows standard locations for ECG electrodes.

FIG. 30 depicts an exemplified flow chart for the ECG assisted potassiumconcentration monitoring.

FIG. 31 depicts a timing diagram of exemplified pulse trains.

FIG. 32 depicts an exemplified experimental setup.

FIG. 33 demonstrates time (in milli-seconds) versus force (arbitraryunits) trace of the skeletal muscle while pulse stimulation according tothe Example(s).

FIG. 34 demonstrates selection of data points computing mechanicalresponses from a pressure strain gauge.

FIG. 35 shows the computed response CR1 as a function of the serumpotassium concentration.

FIG. 36 shows the ECG markers.

FIG. 37 shows an exemplified calculation of T-slope.

FIG. 38 shows changes in R-wave amplitude (in arbitrary units) duringdialysis on best lead.

FIG. 39 shows changes in T-wave amplitude (in arbitrary units duringdialysis on best lead.

FIG. 40 shows change in T/R amplitude ratio during dialysis on bestlead.

FIG. 41 shows change in T-wave flatness during dialysis on best lead.

FIG. 42 shows change in T-slope during dialysis on best lead.

FIG. 43 shows R-wave amplitude (in arbitrary units) during dialysis onlead II.

FIG. 44 shows change in T-wave amplitude (in arbitrary units) duringdialysis on lead II.

FIG. 45 shows T/R amplitude ratio during dialysis on lead II.

FIG. 46 shows change in T-slope during dialysis on lead II.

FIG. 47 shows T-wave flatness during dialysis on lead II.

DETAILED DESCRIPTION OF THE INVENTION

Unless defined otherwise, all technical and scientific terms used hereingenerally have the same meaning as commonly understood by one ofordinary skill in the relevant art.

The articles “a” and “an” are used herein to refer to one or to morethan one (i.e., to at least one) of the grammatical object of thearticle. By way of example, “an element” means one element or more thanone element.

“Chronic kidney disease” (CKD) is a condition characterized by the slowloss of kidney function over time. The most common causes of CKD arehigh blood pressure, diabetes, heart disease, and diseases that causeinflammation in the kidneys. Chronic kidney disease can also be causedby infections or urinary blockages. If CKD progresses, it can lead toend-stage renal disease (ESRD), where the kidneys function is inadequateto sustain life without supplemental treatment.

The terms “communicate” and “communication” include but are not limitedto, the connection of system electrical elements, either directly orwirelessly, using optical, electromagnetic, electrical or mechanicalconnections, for data transmission among and between said elements.

The term “comprising” includes, but is not limited to, whatever followsthe word “comprising.” Thus, use of the term indicates that the listedelements are required or mandatory but that other elements are optionaland may or may not be present.

The term “consisting of” includes and is limited to whatever follows thephrase the phrase “consisting of.” Thus, the phrase indicates that thelimited elements are required or mandatory and that no other elementsmay be present.

A “control system” consists of combinations of components that acttogether to maintain a system to a desired set of performancespecifications. The performance specifications can include sensors andmonitoring components, processors, memory and computer componentsconfigured to interoperate.

A “controller” or “control unit” is a device which monitors and affectsthe operational conditions of a given system. The operational conditionsare typically referred to as output variables of the system, which canbe affected by adjusting certain input variables.

A “subject” or “patient” is a member of any animal species, preferably amammalian species, optionally a human. The subject can be an apparentlyhealthy individual, an individual suffering from a disease, or anindividual being treated for an acute condition or a chronic disease.

The term “programmable” as used herein refers to a device using computerhardware architecture and being capable of carrying out a set ofcommands, automatically.

The term “sensory unit” refers to an electronic component capable ofmeasuring a property of interest.

The terms “treating” and “treatment” refer to the management and care ofa patient having a pathology or condition. Treating includesadministering one or more embodiments of the present invention toprevent or alleviate the symptoms or complications or to eliminate thedisease, condition, or disorder.

As used herein, “treatment” or “therapy” refers to both therapeutictreatment and prophylactic or preventative measures. “Treating” or“treatment” does not require complete alleviation of signs or symptoms,does not require a cure, and includes protocols having only a marginalor incomplete effect on a patient.

Electrocardiogram or ECG is a time varying waveform, produced by theelectrical activity of the cardiac muscle and the associated electricalnetwork within the myocardium. Term is used interchangeably for thetracing that is available from the surface of the subject, or from animplantable or external device.

The term “P-R interval” refers to the length of time from the beginningof the P wave to the beginning of the QRS complex.

The term “QRS width” refers to the length of time of the QRS complex.

The term “Q-T interval” refers to the length of time from the beginningof the QRS complex to the end of the T-wave.

The term “Q-T dispersion” refers to the difference between the maximumand minimum QT intervals measured in a time period.

The term “P-wave amplitude” refers to the maximum potential reached bythe P-wave.

The term “T-wave amplitude” refers to a numerical measurement of themagnitude of the portion of the electrocardiographic representation ofthe repolarization of the ventricles of the heart.

The term “R-wave amplitude” refers to a numerical measurement of themagnitude of the portion of the electrocardiographic signalcorresponding to the depolarization of the ventricles of the heart.

The term “T-slope” refers to a numerical measurement corresponding tothe slope of the line drawn from the peak of the T-wave to the end ofthe T-wave. The end of the T wave is defined as the intercept betweenthe isoelectric line with the tangent drawn through the maximum downslope of the T wave.

The term “ratio of T-wave amplitude to R-wave amplitude (T/R ratio)”refers to the numerical ratio of the T-wave amplitude to the R-waveamplitude.

The term “T-wave flatness” refers to a numerical representation of thedegree that an electrocardiographic T-wave has a low amplitude and ismore spread out and less peaked.

The term “P-wave peak” refers to the rate of change in the P wave inunits of potential change per unit time.

The term “S-T segment” refers to the interval between the QRS complexand the beginning of the T wave. S-T segment is depressed if it has adownward concavity.

The term “T wave” refers to the wave after the QRS complex and the S-Tsegment. An inverted T wave has a negative amplitude.

The term “U wave amplitude” refers to the maximum potential of a wavethat follows the T wave. The U wave is not always observed in a cardiaccycle.

The term “heart rate variability” refers to the time difference betweenthe peaks of R-waves over time in cardiac cycles.

The term “scalar quantity” or “scalar value” refers to a property, valueor quantity that is completely expressed in terms of magnitude.

The term “feature,” “cardiac feature,” “ECG feature” or “feature of acardiac cycle” refers to a property of the a cardiac cycle, as observedby ECG or other means, that is reducible to numerical form. Featuresinclude, but are not limited to, P-R interval, QRS width, Q-T interval,P-wave amplitude, S-T segment depression, T wave inversion, U waveamplitude and T wave amplitude.

The term “feature value” refers to a feature of a cardiac cycleexpressed as a scalar quantity or qualitative property such as depressedor inverted.

The term “feature score” refers to a feature value that has beenconverted to a common scale.

The term “common scale” refers to a unitless scale for expressingfeature values where the common scale has a minimum possible value and amaximum possible value and the feature values differ in units or lack acommon range of magnitude. In some embodiments, the common scale has aminimum value of 0 and a maximum value of 1.

The term “determinant” or “determinate value” refers to a quantity orcriterion that a feature value or feature score is compared to for thepurposes of calculating a risk score.

The term “risk score” or “disease risk score” refers to value calculatedwith one or more feature values or scores that indicates an undesirablephysiological state of the patient. The term “risk score” in certaininstances refers to a numerical representation of the current degree ofthe risk a patient is at for a particular disease, condition, or adverseevent, such as hospitalization, life threatening arrhythmias or death.

The term “exponential factor,” “value k,” or “variable k” refers to amodifiable variable present in an exponent (e.g. e^(k)) in acomputational procedures used to convert a feature value to a featurescore.

The term “weighting factor” or “weighting coefficient” refers to anadjustable coefficient to terms for addition to calculate a disease riskscore.

The term “hypokalemia” refers to a physiological state wherein theconcentration of potassium ions in the blood serum or interstitial fluidis less than the normal physiological range of 3.5 to 5 mEq/L.

The term “hyperkalemia” refers to a physiological state wherein theconcentration of potassium ions in the blood serum or interstitial fluidis more than the normal physiological range of 3.5 to 5 mEq/L.

“Kidney disease” (KD) is a condition characterized by the slow loss ofkidney function over time. The most common causes of KD are high bloodpressure, diabetes, heart disease, and diseases that cause inflammationin the kidneys. Kidney disease can also be caused by infections orurinary blockages. If KD progresses, it can lead to end-stage renaldisease (ESRD), where kidney function is inadequate to sustain lifewithout supplemental treatment. KD can be referred to by differentstages indicated by Stages 1 to 5. Stage of KD can be evaluated byglomerular filtration rate of the renal system. Stage 1 KD can beindicated by a GFR greater than 90 mL/min/1.73 m² with the presence ofpathological abnormalities or markers of kidney damage. Stage 2 KD canbe indicated by a GFR from 60-89 mL/min/1.73 m², Stage 3 KD can beindicated by a GFR from 30-59 mL/min/1.73 m² and Stage 4 KD can beindicated by a GFR from 15-29 mL/min/1.73 m². A GFR less than 15mL/min/1.73 m² indicates Stage 5 KD or ESRD. It is understood that KD,as defined in the present invention, contemplates KD regardless of thedirection of the pathophysiological mechanisms causing KD and includesCRS Type II and Type IV and Stage 1 through Stage 5 KD among others.Kidney disease can further include acute renal failure, acute kidneyinjury, and worsening of renal function. In the Cardiorenal Syndrome(CRS) classification system, CRS Type I (Acute Cardiorenal Syndrome) isdefined as an abrupt worsening of cardiac function leading to acutekidney injury; CRS Type II (Chronic Cardiorenal syndrome) is defined aschronic abnormalities in cardiac function (e.g., chronic congestiveheart failure) causing progressive and permanent kidney disease; CRSType III (Acute Renocardiac Syndrome) is defined as an abrupt worseningof renal function (e.g., acute kidney ischaemia or glomerulonephritis)causing acute cardiac disorders (e.g., heart failure, arrhythmia,ischemia); CRS Type IV (Chronic Renocardiac syndrome) is defined askidney disease (e.g., chronic glomerular disease) contributing todecreased cardiac function, cardiac hypertrophy and/or increased risk ofadverse cardiovascular events; and CRS Type V (Secondary CardiorenalSyndrome) is defined as a systemic condition (e.g., diabetes mellitus,sepsis) causing both cardiac and renal dysfunction (Ronco et al.,Cardiorenal syndrome, J. Am. Coll. Cardiol. 2008; 52:1527-39).

The term “electromyogram sensor” refers to a device for sensing theelectrical or mechanical activity produced as a result of the voluntaryor stimulated contraction of skeletal muscles of the body.

The term “electrocardiogram sensor” refers to a device for sensing ofthe electrical activity of the heart. It typically consists of a set ofelectrodes along with associated electronics. Electrodes may be applieddirectly to the skin or can be part of an implanted device.

The term “serum potassium concentration” in certain instances refers to“blood potassium concentration”.

The term “communication device” or “communication unit” refers to adevice such as a telemetry system or any other alert system such as anaudio feedback device, which can communicate monitoring results to apatient and/or a medical care personnel as needed. The term“communication device” in certain instances refers to a device whichserves the purpose of sending information with its transmissioncapabilities to another device which receives the information usingreceiving capabilities. It can use electromagnetic, optical or acousticmeans for signal transmission.

The term “pulse generator” refers to an electronic circuit whichgenerates electrical pulses in accordance with a predetermined sequence,to be delivered through electrodes to an external load, typically anorgan such as a skeletal muscle, heart or nerve tissue. In certaininstances, a pulse generator is or includes an electrical pulsegenerator.

The term “pulse-sensing electrodes” refers to devices which can detectthe presence of electrical or mechanical activity of the tissue, such asthe skeletal muscle or nerves, and convert them into electrical signals.

The term “pulsing schedule” refers to a particular scheme of deliveringelectrical pulses at specified times, frequencies, and amplitudes.

The term “amplifier” refers to an electronic device that increases thepower of a signal and provides impedance matching. It can be used toincrease the voltage and/or current produced by the pulse generator,which in turn is delivered to the tissue.

The term “skeletal muscle strain sensor” refers to a device whichdetects the changes in the strain of an artificial device as a result ofchanges in the contractile tone of a skeletal muscle. Output of thistransducer is usually an electrical voltage or a current that isproportional to either the absolute value of the strain or to thechanges in the strain.

The term “blood pressure sensor” refers to a sensor designed to detectthe pressure changes within a pressure cuff worn around an extremity,such as an arm or a leg. Analysis of the pressure waveform produces thesignals correlating to the changes in the contractile tone of a skeletalmuscle as well as the information relating to the blood pressure such asthe diastolic and systolic blood pressure and heart rate.

The term “lead II” refers to the electrocardiographic voltage signalbetween the left leg electrode and the right arm electrode.

The term “lead V2” refers to the electrocardiographic voltage signalbetween an electrode located in the fourth intercostal space (betweenribs 4 and 5) just to the left of the sternum, and Wilson's centralterminal, which is the average of the three limb leads (I, II, and III).Leads I, II and III are the voltages between the left arm and right armelectrode, between the left leg and right arm, and between the left legand left arm electrode, respectively.

The term “lead V3” refers to the electrocardiographic voltage signalbetween an electrode located centrally between the V2 and V4 electrodes,and Wilson's central terminal, which is the average of the three limbleads (I, II, and III). Leads I, II, and III are the voltages betweenthe left arm and right arm electrode, between the left leg and rightarm, and between the left leg and left arm electrode, respectively.

The term “lead V4” refers to the electrocardiographic voltage signalbetween an electrode located in the fifth intercostal space (betweenribs 5 and 6) in the mid-clavicle line, and Wilson's central terminal,which is the average of the three limb leads (I, II, and III). Leads I,II and III are the voltages between the left arm and right armelectrode, between the left leg and right arm, and between the left legand left arm electrode, respectively.

Monitoring of Dialysis Treatment

As discussed above, a patient's serum potassium level can be unstableand/or drift after dialysis treatment. Due to the requirement for properpolarization for cardiac function, changes in potassium serum levelsafter treatment are a contributor to arrhythmias and other cardiaccomplications in patients undergoing kidney dialysis therapy. Duringdialysis treatment, small solutes in the blood or other body fluids,such as potassium ions, freely interchange with a dialysate fluid.However, due to the action of the sodium-potassium pump, the vastmajority of potassium in the body is present intracellularly and notdirectly accessible during dialysis. Due to the sequestering ofpotassium within cells, potassium serum levels can change significantlyfollowing dialysis treatment sessions. Specifically, dialysis treatmentcan enhance the movement of potassium ions into the cells, which canefflux out of the cells following treatment leading to significantchanges in potassium ion concentration over time.

Normal serum potassium level ranges from 3.5 to 5 mEq/L, wherein adialysate solution is at a lower concentration to drive the movement ofpotassium ions from the serum to the dialysate. As dialysis functions toremove potassium ions from the blood serum as a result of aconcentration gradient between the patient's blood serum and thedialysate, additional potassium ions are drawn out from cells into theintracellular fluids to provide for further removal of potassium ions.However, the movement of potassium ions from inside cells to theextracellular fluids is not consistent in all patients. In particular,acid-base balance can affect the influx and efflux of potassium ionsfrom cells. Tonicity, glucose and insulin concentrations andcatecholamine activity also affect the balance of potassium betweencells and the extracellular fluid. Patients can experience slightalkalosis during at the beginning of dialysis treatment, which canpersist during a multi-hour dialysis treatment. Alkalosis is caused bythe bicarbonate present in the dialysate, which acts as a pH buffer.During alkalosis, it is possible for intracellular potassium ionconcentrations to increase even while the serum potassium ionconcentration is simultaneously being reduced by dialysis. As such, therate of potassium removal is not uniform during dialysis.

At the end of dialysis treatment, an efflux of intracellular potassiumback into the blood serum can result in hyperkalemia. Hyperkalemia canalso occur through the accumulation of potassium in the patient's diet.Conversely, potassium in the blood serum can remain low followingdialysis resulting in hypokalemia. The innovations disclosed hereinenable the monitoring of a patient's serum potassium level duringdialysis, after dialysis or both during and after dialysis. In certainembodiments, ECG signals from the patient can be evaluated to determinepotassium status. For example, hyperkalemia can cause a reduction in Pwave amplitude, peaked or inverted T waves as well as changes in thetime width of the QRS complex.

Using the innovations described herein, a patient can be monitored forpotentially life-threatening hyperkalemia or hypokalemia after adialysis session possibly before the patient becomes aware of symptoms.In certain embodiments, the information gained regarding the patient'sblood serum potassium levels following dialysis can be used to adjustdialysis treatments provided to that patient. For example, a patientthat shows a pattern of a high serum potassium levels after dialysistreatment be administered treatment where the amount of potassium saltin the dialysate fluid is adjusted, for example by a gradient, from ahigh concentration at the beginning of dialysis to a lower concentrationat the end of dialysis to reduce the large changes in potassium plasmalevels during treatment that can result in hyperkalemia. Alternatively,a patient showing a tendency toward hyperkalemia can receive morefrequent treatments and/or more frequent treatments of shorter durationto affect a greater degree of potassium removal. A patient can even beadvised to modify their diet passed upon blood serum potassium levelsfollowing dialysis. Similarly, a patient showing a tendency towardhypokalemia following dialysis can receive less frequent treatment ortreated with a dialysate fluid having a higher concentration ofpotassium salt.

In some embodiments, serum potassium concentration, electrolyte levelsand or pH can be monitored before and/or during a dialysis treatment forbetter management of electrolytes, including potassium, in the patient.Any suitable transducer or sensor can be employed to detect pH orvarious electrolytes in the blood prior to initiation of a dialysistreatment. In embodiments, the transducer or sensor is an ion-selectiveelectrode configured to detect H⁺ ions (pH), K⁺ ions, Na⁺ ions, Ca²⁺ions, Cl⁻ ions, phosphate ions, magnesium ions, acetate ions, aminoacids ions, or the like. Data from the pH and/or ion sensors/electrodescan be employed to appropriately select an initial dialysate compositionprior to the beginning of a dialysis treatment. Data acquired from thesensors can be transmitted to a processor or other device or devices incommunication with a dialysis treatment system, wherein the initial pHand electrolyte composition of a dialysate or a replacement fluid can beadjusted. The pH and electrolyte concentration of the fluid (dialysateor replacement fluid) can be adjusted in any suitable manner.

In particular, data from pH and/or ion sensors/electrodes can betransmitted to be available to a healthcare provider through theprocessor or other device and used to adjust the concentration ofelectrolytes or pH in a dialysate or replacement fluid. In someembodiments, the dialysate is generated from water or alow-concentration solution present in a dialysate circuit in fluidcommunication with the patient, wherein one or more pumps controls theaddition of one or more infusate solutions to the dialysate circuit toconstitute a desired dialysate immediately prior to contact with thepatient or a hemodialyzer. The dialysate can be constitute to affect aspecific mass transfer of electrolytes from the blood of a patient tothe dialysate or from the dialysate to the blood of a patient in amanner to correct any determined electrolyte imbalances or non-idealelectrolyte ranges. Similarly, the amount of a buffer, such asbicarbonate, in the dialysate can be adjusted to vary the amount ofbicarbonate uptake by the patient during treatment.

Medical Device

The medical systems and medical devices of the present invention monitorphysiological signals from patients. The medical devices provide manyadvantages including full patient compliance, complete patient mobility,lower maintenance requirements and lower chances for device relatedinfections. The medical devices can be powered with internal batteriesand can be implanted or external to the body. Data transmission to andfrom the devices is accomplished by electromagnetic or electroconductivetelemetry means. In embodiments of the invention, the medical devicescontain one or multiple sets of sensors. For example, the devices cansense the ECG of a patient and change in activity or posture of thepatient. The sensed signals can be stored in memory and transmitted viaradio telemetry. Furthermore, the processor units within the medicaldevices can be used to process the detected or recorded signals.

The ECG signals can be processed to extract features from the ECGsignal. These features include but are not limited to P-R interval, QRSwidth, Q-T interval, QT-dispersion, P-wave amplitude, T-wave amplitude,T/R amplitude ratio, T-slope, P-wave peak, S-T segment depression,Inverted T-waves, U-wave observation, T-wave peak amplitude, Heart RateVariability. While some features are measured for each cardiac cyclesuch as the P-R interval, others are calculated as a time average suchas heart rate variability.

Many factors affect the features of the ECG. For example, heart ratevaries as a result of changes in metabolic demand. During exercise, anincreased demand for oxygen causes the heart rate to increase.Correspondingly, the P-R interval decreases during exercise. Anotherfactor that modulates the features of the ECG is changes in theconcentrations of the ions in the body. An ion that modulates the ECGand is important for the management of KD patients is potassium ion. Ingeneral, changes in potassium concentrations manifest as alterations ofsome of the features of the ECG. However, these alterations vary fromone patient to another patient and can necessitate the individualizationof the detection computational procedure as described herein.

In particular, the medical device of the present invention monitors apatient electrocardiogram (ECG) wherein an internal or externalprocessing unit extracts features from the ECG and processes theresulting data. An optional telemetry system or any other alert system,such as an audio feedback device, can communicate the results to thepatient and medical care personnel as needed. In certain embodiments,the device has an electrical pulse generator configured to contact thetissue of a patient such as muscle tissue or cardiac tissue, and asensor to detect a response of the tissue where the response provides anindication of the potassium ion concentration in the extracellularfluid. In another embodiment, the device comprises a pulse generatorconfigured to generate electrical stimulation wherein an electrodedelivers electrical stimulation to a tissue such as a skeletal muscle ina patient. The device can include a sensor configured to detect at leastone response of the tissue to electrical stimulation, and a processorconfigured to determine a concentration of potassium ions in theextracellular fluid of the patient as a function of the response. Inparticular, the processor can be configured to determine a concentrationof potassium ions as a function of a sustained contraction of thetissue, for example, or a rippled contraction of the tissue, a rate ofrelaxation of the tissue, a pulse width of the response, the occurrenceof summation in the response or the amplitude of the response. Thesystem can be external, partially implantable or fully implantable.Notably, a healthy level of potassium in the human blood is about 3.5-5mEq/L, but in patients with KD, the concentration could rise to 6-8 mM.Most patients are dialyzed with hypo-osmotic dialysate solutions wherethe potassium concentration is fixed at a hypo-osmotic level, such as 2mM, to assure the transfer of potassium ions from the patient's bloodinto the dialysate solution.

The medical device can be a unit with no leads or may contain leads andexternal sensors. Units with no leads such as the Medtronic Reveal®device, or other known devices familiar to those of ordinary skill, mayhave electrodes for sensing electrocardiograms or for deliveringelectrical stimulation. Units with leads, such as pacemakers, cardiacresynchronization devices and defibrillators, utilize their leads forsensing electrocardiograms. The medical device may also have othersensors, such as an internal accelerometer and an external pressuresensor, which is external to the device yet still reside inside thepatient. The device can contain a power source such as a battery, acomputing hardware, a data storage unit such as electronic memory andcommunication hardware or related systems.

FIG. 1 presents an embodiment of an implantable medical device that maybe used to obtain ECG data without the use of leads. However, externalembodiments are contemplated by the invention. A monitor 10 is implantedsubcutaneously in the upper thoracic region of the patient's body 18near the patient's heart 16. The monitor 10 comprises a non-conductiveheader module 12 attached to a hermetically sealed enclosure 14. Theenclosure 14 contains the operating system of the monitor 10 and ispreferably conductive but can be covered in part by an electricallyinsulating coating. A first, subcutaneous, sensing electrode A is formedon the surface of the header module 12 and a second, subcutaneous,sensing electrode B is formed by an exposed portion of the enclosure 14.A feed-through extends through the mating surfaces of the header module12 and the enclosure 14 to electrically connect the first sensingelectrode A with the sensing circuitry (not shown) within the enclosure14, and the conductive sensing electrode B directly to the sensingcircuitry.

The electrical signals attendant to the depolarization andre-polarization of the heart 16 referred to as the ECG are sensed acrossthe sensing electrodes A and B. The monitor 10 is sutured tosubcutaneous tissue at a desired orientation for electrodes A and Brelative to the axis of the heart 16 to detect and record the ECG in asensing vector A-B for subsequent uplink telemetry transmission to anexternal programmer (not shown). FIG. 1 shows only one possibleorientation of the sensing electrodes A and B and sensing vector A-B. Itwill be understood by those of ordinary skill in the art that additionalorientations are possible. The hermetically sealed enclosure 14 includesa battery, circuitry that controls device operations and records ECGdata in memory registers, and a telemetry transceiver antenna ortransceiver electrodes and circuit that receives downlink telemetrycommands from and transmits stored data in a telemetry uplink to theexternal programmer. The circuitry and memory can be implemented indiscrete logic or a micro-computer based system with Analog/Digitalconversion of sampled ECG amplitude values.

As depicted in FIG. 2, an implantable medical device (IMD) 13 is amulti-chamber pacemaker that can both deliver electrical stimulation andmonitor potassium levels, as described in U.S. Patent Publication2006/0217771 A1, the contents of which are incorporated in theirentirety. The dual capability of IMD 13 is particularly well suited forpatients suffering from cardiac disease requiring pacing and concomitantkidney disease requiring monitoring of potassium concentrations fordialysis. The exemplary embodiment can deliver electric stimulation andrecord ECG data in the heart 15 of a patient. A right ventricular lead17 has an elongated insulated lead body carrying one or more concentriccoiled conductors separated from one another by tubular insulatedsheaths. The distal end of right ventricular lead 17 is deployed in theright ventricle 19 of heart 15. Located adjacent to the distal end ofthe lead body are one or more pacing/sensing electrodes 20, which areconfigured to deliver cardiac pacing and are further configured to sensedepolarizations of right ventricle 19. A fixation mechanism 22, such astines or a screw-in element anchors the distal ends in right ventricle19. The distal end also includes an elongated coil electrode 24configured to apply cardioversion or defibrillation therapy. Each of theelectrodes is coupled to one of the coiled conductors within the leadbody. At the proximal end of right ventricular lead 17 is a connector26, which couples the coiled conductors in the lead body to IMD 13 via aconnector module 28. A right atrial lead 30 includes an elongatedinsulated lead body carrying one or more concentric coiled conductorsseparated from one another by tubular insulated sheaths corresponding tothe structure of right ventricular lead 17. Located adjacent theJ-shaped distal end of right atrial lead 30 are one or morepacing/sensing electrodes 32, which are configured to sensedepolarizations and deliver pacing stimulations to right atrium 34.

Also shown in FIG. 2 is an elongated coil electrode 36 proximate to thedistal end of right atrial lead 30, and located in right atrium 34 andthe superior vena cava 38. At the proximal end of the lead is aconnector 40, which couples the coiled conductors in right atrial lead30 to IMD 13 via connector module 28. A coronary sinus lead 42 includesan elongated insulated lead body deployed in the great cardiac vein 44.The lead body carries one or more coiled conductors coupled to one ormore pacing/sensing electrodes 46. Electrodes 46 are configured todeliver ventricular pacing to left ventricle 48 and are furtherconfigured to sense depolarizations of left ventricle 48. Additionalpacing/sensing electrodes (not shown) may be deployed on coronary sinuslead 42 that are configured to pace and sense depolarizations of theleft atrium 50. At the proximal end of coronary sinus lead 42 isconnector 52, which couples the coiled conductors in coronary sinus lead42 to connector module 28. An exemplary electrode element 54A is coupledto the distal end of a lead 56. Lead 56 carries one or more conductorsseparated from one another by insulated sheaths. A connector 58 at theproximal end of the lead couples the conductors in lead 56 to IMD 13 viaconnector module 28. In addition to connector module 28, IMD 13 has ahousing 60 formed from one or more materials, including conductivematerials such as stainless steel or titanium. Housing 60 can includeinsulation, such as a coating of Parylene® (poly(p-xylylene)) orsilicone rubber, and in some variations, all or a portion of housing 60can be left uninsulated. The uninsulated portion of housing 60 can serveas a subcutaneous electrode and a return current path for electricalstimulations applied via other electrodes.

Also shown in FIG. 2 is electrode element 54A that includes twoelectrodes 62A and 62B. At least one of electrodes 62A and 62B isdeployed in or near test tissue and delivers stimulation to the tissue,while the other provides a return current path. The test tissue cancom-prise a collection of autologous or non-autologous cells that aresensitive to [K⁺]. For example, the test tissue may be one of cardiacmuscle, skeletal muscle, smooth muscle, nerve tissue, skin, or the like.The IMD 13 includes a sensor that detects the electromechanical responseof the muscle to the stimulation delivered by electrodes 62A and 62B.The detected electromechanical response can include muscle tension,muscle strength, muscle density, muscle length and pressure generated bythe muscle. The electromechanical sensor can be incorporated completelywithin the housing of IMD 13 or can be present outside the housing.Example sensors include optical sensors for observing mechanicalresponses and an accelerometer that responds to muscle movement. Furtherembodiments of the sensor for detecting an electromechanical responseinclude pressure sensors and piezoelectric sensors.

In certain embodiments, the accelerometer can have a 3-axisaccelerometer capable of separately detecting heart and lung sounds ormovement and respiration rate. Heart and lung movement and respirationrate can indicate fluid volume overload. Any implantable device toobtaining ECG or other data can also have temperature sensingcapabilities.

FIG. 3 shows graphs of muscle force that illustrate exemplary techniquesto determine a concentration of [K⁺] in extracellular fluid (ECF) as afunction of the response of skeletal muscle to stimulations from anelectrode element such as electrode elements 54A. Each stimulus can havean amplitude of about 2 to about 20 Volts, for example, and a pulsewidth of about 0.1 to 1.0 milliseconds. Stimulus line 100 shows thetiming of stimuli delivered to the skeletal muscle via electrodes suchas electrodes 62A-B of FIG. 2. Response line 102 depicts a response ofskeletal muscle to the stimulations in an environment where [K⁺] is lowrelative to concentrations in intracellular fluid (ICF). In other words,response line 102 depicts a response of skeletal muscle in a “normal”patient. By contrast, response line 104 depicts a response of skeletalmuscle in a patient having elevated [K⁺].

The frequency of stimuli can vary from about 10 to about 150 Hz. Musclein a normal environment has longer duration contractions and can exhibitsome summation. Muscle contractions in a lower [K⁺] environment have alarger amplitude and have a longer duration than a high [K⁺]environment. As described in FIG. 3, data obtained from electricalstimulation of potassium-sensitive tissue can be used to supplement theanalysis of ECG data described herein.

In one or more embodiments, a medical system is provided for monitoringserum potassium concentration in a subject. The medical system includesa medical device such as the medical device 410 referenced in FIG. 9,wherein the medical device includes at least one of an electromyogramsensor and an electrocardiogram sensor for detecting a change in muscleor nerve activity of the subject and for producing at least oneelectrical signal based on the change in muscle or nerve activity asdetected, the electrical signal being indicative of a serum potassiumconcentration of the subject. Thereafter, a processor such as theprocessor 430 referenced in FIG. 9 may be used to apply a forwardcomputational procedure as detailed herein elsewhere to the at least oneelectrical signal to generate a risk score, and a communication deviceas detailed herein elsewhere may be used to issue an alert whichindicates a condition of hyperkalemia, hypokalemia or arrhythmia of thesubject based on the risk score. The communication device may be thecommunication system referenced in FIG. 9 allowing the transfer of dataand mediating the data transfer between the medical device 410 and theprocessor 430.

As detailed herein below, the present invention in one or moreembodiments is advantageous in providing a non-invasive method andsystem for monitoring serum potassium concentration in a subject. Inparticular, one or more of these ECG features can be significantnon-invasive markers or indicators for monitoring corresponding serumpotassium concentration. Without wanting to be limited to any particulartheory, it is believed, and at least with the T-wave measurements, theT-wave measurement can be specific in determining the details ofrepolarization once it begins, which is potassium sensitive. Forinstance also, the R-wave is believed to be sensitive to the differencebetween intracellular and extracellular potassium. In this connection,and at least in certain particular instances, the T-wave amplitude, theR-wave amplitude, and/or the T/R amplitude ratio may be more sensitiveto other ECG features such as the P-R interval and the QT interval asmarkers or indicators for monitoring serum potassium concentration. Thismay be because the P-R interval and the QT interval reflect more on theeffects of autonomic nervous system rather than on potassiumconcentration variations.

The at least one electromyogram sensor and/or the at least oneelectrocardiogram sensor may be positioned within the medical device inany suitable way or at any suitable posi-tion. In certain instances, theat least one electromyogram sensor and/or the at least oneelectrocardiogram sensor may be similarly positioned within the medicaldevice like the electrodes 62A and/or 62B referenced in FIG. 2.

As illustratively depicted in FIG. 27, a medical system generally shownat 2710 may include: a pulse generator 2711 for producing one or morepulse sets; one or more pulse-sensitive electrodes 2712 for mediatingcommunication between the pulse generator 2711 and a subject 2720; amedical device 2714 for generating and/or receiving a signal indicatingan extent of muscle strain of the subject 2720 upon a contact with theone or more pulse sets; and a processor 2716 for receiving the signalfrom the medical device 2714 and producing an output on serum potassiumconcentration based on the signal.

Referring back to FIG. 27, the pulse generator 2711 may be an electroniccircuit producing a digital signal having only two levels correspondingto 0 and 1 levels, or OFF and ON. Duration of each interval and numberof times to repeat them are predetermined by an algorithm and can bechanged if necessary. Such a pattern can be generated using a logicgates, timer circuits or programmable devices such as a computer or amicroprocessor. A non-limiting example of the pulse generator 2711 maybe an Arduino microprocessor.

In this design, the medical device 2714 includes at least oneelectromyogram sensor to detect the extent of muscle strain of thesubject 2720 in response to the one or more pulse set mediated by thepulse-sensing electrode 2712. In certain instances, the at least oneelectromyogram sensor of the medical device 2714 may be a pressuresensor or a blood pressure cuff.

The pulse generator may be provided with a pulsing schedule such thatthe one or more pulse sets include a first pulse set and a second pulseset, the first and second pulse sets are produced according to one ormore of the following algorithm rules: i) the first and second pulsesets are separated in time by 0.5 to 5 seconds; ii) the first and secondpulse sets each independently include 3 to 10 pulses; and iii) the firstand second pulse sets each independently having a frequency of 2 to 50.

The present invention in one or more embodiments is unique in usingburst stimulation to generate and monitor the ripples on the muscleresponse and the subsequent interpreta-tion of these ripples to derivethe relation to the serum potassium concentration are unique prop-ertiesof the algorithm. In doing so, the number of pulses and the frequency ofthe pulses may need to be chosen carefully. For example, if there aretoo few pulses, then the measured muscle response is the transient one,not the steady state one. If there are too many pulses, one may riskthat the muscle will fatigue, giving an erroneous response. Similarly,the frequency of the stimulation must be low enough to prevent immediateformation of tetnus which eliminates the ripples on the contraction. Atthe same time, stimulus that is delivered at too low frequencies do notresult in the fusion of the contraction, hence would not allow theanalysis of the ripples. Even though the present algorithm suggests theuse of N=5 pulses delivered at f=5 Hz, deviations from those numbers arewithin the scope of the present invention.

The operation of the pulse generator may be triggered by on its ownusing a self-timer, by the monitor, or by the medical care provider. Itmay also be triggered when there is blood draw for analysis, which canbe used for the calibration of the sensory.

The pulsing schedule of the pulse generator may be provided such thatthe first and second pulse sets are produced according to two or more ofthe following pulsing rules: i) the first and second pulse sets areseparated in time by 1.5 to 2.5 seconds; ii) the first and second pulsesets each independently include 4 to 6 pulses; and iii) the first andsecond pulse sets each independently having a frequency of 2 to 10 Hz.

The first and second pulse sets are only illustrative of the pulsingschedule in that the pulsing schedule may include more than two pulsesets. In particular, the pulsing schedule may include three, four, fiveor more pulse sets with each of them independently including feature(s)described in relation to the first or second pulse set.

Regarding the pulsing rule i), the first and second pulse sets may beseparated in time by 0.5 to 5 seconds, 0.75 to 4.25 seconds, 1.0 to 3.5second, or 1.5 to 2.75 seconds. The separation in time between the firstand second pulse sets may be measured by the distance in time betweenthe first peak of the first pulse set and the first peak of the secondpulse set. The separation in time between two adjacent pulse sets may beadjusted accordingly based on the specifics of a project at hand.However, these separation in time values may be particularly useful forcarrying out the serum potassium concentration monitoring intended bythe present invention in one or more embodiments

Regarding the pulsing rule ii), the first and second pulse sets may eachindependently include 3 to 10 pulses, with each pulse observable withthe presence of a peak, or 3 to 8 pulses, or 4 to 6 pulses. The totalnumber of pulses or peaks contained within each of the pulse sets may beadjusted accordingly based on the specifics of a project at hand.However, these pulse numbers may be particularly useful for carrying outthe serum potassium concentration monitoring intended by the presentinvention in one or more embodiments.

Regarding the pulsing rule iii), the first and second pulse sets mayeach independently be of a frequency of 2 to 50 Hz, 2 to 40 Hz, 2 to 30Hz, 2 to 20 Hz, or 2 to 10 Hz. The pulsing frequency may be adjustedaccordingly based on the specifics of a project at hand. However, thesefrequency ranges may be particularly useful for carrying out the serumpotassium concentration monitoring intended by the present invention inone or more embodiments.

Referring back to FIG. 27, the medical system 2710 may further includean amplifier 2718 mediating communication between the pulse generator2711 and the one or more pulse-sensing electrodes 2712.

In certain instances, the amplifier 2718 may be capable of amplifyingand delivering the pulses to tissues with unknown load impedance. It ispossible that the load impedance of the tissue can be as low as 20 Ohms,and as high as 50 kilo-ohms. Similarly, the output voltage of theamplifier is adjustable from 1 Volt to 10 Volts, preferably at 8 Volts.In order to minimize the patient discomfort, the pulsing schedule mayneed to be kept at least initially at a relatively low value in voltage,such as 2 Volts. If there is no response from the tissue, then theoutput voltage is increased until an evoked response is observed.Furthermore, the amplifier may also need to have a broad frequencyresponse, from 0.1 Hz to 1 kHz to minimize the distortion of thedelivered pulses. In addition, and in certain instances, the amplifiermay need to provide the required electrical isolation necessary for allpatient connected electrical medical devices to reduce the riskaccidental electrocution of the subject.

In certain embodiments, and as depicted in FIG. 28, a medical device2814 may include one or more electrocardiogram (ECG) electrodes 2812 forreceiving one or more electrocardiogram features from the subject 2820,the one or more electrocardiogram features including T-wave amplitude,R-wave amplitude, T-slope, ratio of T-wave amplitude to R-wave amplitude(T/R ratio), and T-wave flatness; and an ECG algorithm for producing anoutput on the serum potassium concentration in the subject 2820, basedon an input including the one or more electrocardiogram features,wherein the ECG algorithm includes one or more of the followingoperational rules: i) the output on the serum potassium concentrationbeing a function of the R-wave amplitude; ii) the output on the serumpotassium concentration being a function of the T-wave amplitude; iii)the output on the serum potassium concentration being a function of theT/R ratio; and iv) the output on the serum potassium concentration beinga function of the T-wave flatness.

Myocardial cellular action potentials are formed by the flow ofpositively charged ions such as sodium, potassium, and calcium throughthe cellular membranes. The ECG is made up of the aggregation ofelectrical signals from many myocardial cells. FIG. 29 shows standardlocations for ECG electrodes. Lead II is the voltage between the leftleg (LL) electrode and the right arm (RA) electrode. Leads V1 through V6are termed the “precordial” leads, and the negative electrode thereto isthe average of the three limb leads (I, II, and III). Leads I and IIIare the voltage between the left arm and right arm electrode and betweenthe left leg and left arm electrode, respectively. The ECG features maybe detected according to the schedules tabulated in Table 4.

TABLE 4 Schedules for Detecting ECG Features Feature ECG FeatureIdentification Schedule R-wave Find maximum amplitude (either positiveor negative) of segment in identification R-wave template. Store thepolarity of this segment. Find threshold points in the data stream thatexceed 70% of the template maximum amplitude. Look at the data segmentfrom the first threshold point encountered looking out for the width ofthe R-wave template. If the detection is not too wide or flat, it isconsidered a valid R-wave. This is done by checking if the slopes athalf of the template width on either side of the candidate R-wave peakare at least 60% of the corresponding slopes on the template signal.Skip ahead (blank) for 1.5* template width before searching for the nextR wave. Store the intervals between R waves also (RR intervals). P-wavestart Starting 20 ms before the Q time, store all the samples for theprevious 0.25*RR interval. Sort the samples in ascending order. Storethe times of the largest 10% of samples, and then start at the initialtime of this P-wave segment. Looking back for 30 ms, find the maximumslope over a 20 ms interval. Then find the earliest point where theupslope of the p-wave exceeds ½ of the maximum slope. P-wave peak Findthe width of the p-wave segment with the largest 10% of samples, asdescribed above. Set the P-peak time to the middle of this segment.R-wave start Look back from the peak of the R wave for 100 ms. (Q time)The Q point is detected when the slope decreases to the point that thedifference in signal amplitude over 18 ms is less than 1/50 of theR-wave amplitude for several samples. R-wave peak Point of maximumamplitude away from 0, either in positive or negative direction, withintemplate width after the R-wave detection point. R-wave end Find minimumamplitude within 46 ms after R wave peak. Find maximum slope and minimumnonnegative slope within 50 ms after the minimum amplitude. Look outbeyond the minimum amplitude point for the point where the slope is 1/10of the way from the minimum to the maximum slope. T-wave start End ofR-wave T-wave peak Find maximum positive amplitude during the intervalstarting 150 ms after the R-wave peak through the point 0.4*{square rootover (RR interval)} after that. Look through that same window, store allthe samples, and sort them in ascending order. Find the times of all thesamples in the top 10%. Pick the center of all those times. T-wave endFind the minimum amplitude between 130 and 180 ms after the T-wave peak.Use this as the isoelectric line. Find the steepest slope over a 15 mssegment between the T-wave peak and 0.2*RR interval after it.Interpolate the maximum slope from the point where it is measured to theisoelectric line. Store that point as the end of the T-wave.

The one or more electrocardiogram electrodes may include one or more oflead II, lead V2, lead V3 and lead V4, which are illustratively depictedin FIG. 29. The rationale for selecting these leads may be that the ECGchange which is usually first observable given abnormal potassium levelsis the appearance of peaked, symmetric T-waves. T-waves are largest onthe precordial leads, and T-wave changes due to hyperkalemia are mostlikely seen on V2, V3 and V4.

In certain instances, the one or more electrocardiogram electrodesconsist of lead II only. Lead II is examined because it is closest tothe Reveal signal which is optionally useful for certain chronic kidneydisease monitoring projects.

The output on the serum potassium concentration, as directly orindirectly obtain-able from the medical device referenced in FIG. 27and/or the medical device referenced in FIG. 28 may be a differencebetween a serum potassium concentration at time t₁ of the subject and abaseline potassium concentration at time t₀ of the subject, time t₁being at least 10 minutes, 20 minutes, 30 minutes, 40 minutes, 50minutes, 60 minutes, 120 minutes, 12 hours, 24 hours, 48 hours, or 96hours, apart from t₀. The time period between time t₀ and time t₁ may bea time interval within a medical treatment session such as a dialysissession, during which time t₀ represents an earlier time point duringthe dialysis and time t₁ represents a later time point during thedialysis. This is useful as the subject can be monitored in real timefor serum potassium concentration via the use of the at least oneelectromyogram sensor and/or the at least one electrocardiogram sensorcontained within or connected to the medical device such that thesubject may be spared of the inconvenience and sometimes pain associatedwith periodic blood draws otherwise needed for conventional potassiumconcentration monitoring.

The baseline serum potassium concentration may be a value selected fromthe group consisting of a baseline serum potassium concentration of thesubject obtained at a periodic blood draw, a baseline serum potassiumconcentration of the subject obtained at the onset of a dialysissession, and a baseline serum potassium concentration of the subject atthe end of a dialysis session. In this connection, the baselinepotassium concentration values can be determined at the time of theweekly, biweekly, monthly, or bi-monthly blood draw when the potassiumlevel can be determined accurately. Baseline can also be defined at theonset of the dialysis session, because that is the onset of themeasurement process. It is also possible to define the baseline as themeasurements done at the end of the dialysis session, because at thattime, the potassium value is likely to be within the normalphysiological range, and very close to the dialysate value, which isknown.

In certain instances, one or more baselines for each ECG feature may bedetermined on an individual basis for each subject, for instance, onewhen [K+]=5 mM and one when [K+]=3.5 mM. If this is not possible, thenbaseline may be measured at some time when potassium is within theseranges. It should be noted that the response is fairly linear in theclinically significant range of hyperkalemia, i.e. [K+]=5 mM to [K+]=9mM.

According to the operational rule of the ECG algorithm, the output onthe serum potassium concentration may be in a negative correlation withthe R-wave amplitude. The negative correlation refers to an observationwhere the R-wave amplitude increases as the serum potassiumconcentration decreases in the subject. The negative correlation,however, does not require a straight line correlation with a singleslope. Rather, the negative correlation is found when the beginningvalues of the serum potassium concentration and the R-wave amplituderelative to their corresponding ending values change in the samedirection.

According to the operational rule of the ECG algorithm, the output onthe serum potassium concentration is in a positive correlation with theT-wave amplitude. The positive correlation refers to an observationwhere the T-wave amplitude decreases as the serum potassiumconcentration decreases in the subject. The positive correlation,however, does not require a straight line correlation with a singleslope. Rather, the positive correlation is found when the beginningvalues of the serum potassium concentration and the T-wave amplituderelative to their corresponding ending values change in the samedirection.

According to the operational rule of the ECG algorithm, the output onthe serum potassium concentration is in a positive correlation with theT-slope. The positive correlation refers to an observation where theT-slope decreases as the serum potassium concentration decreases in thesubject. The positive correlation, however, does not require a straightline correlation with a single slope. Rather, the positive correlationis found when the beginning values of the serum potassium concentrationand the T-slope relative to their corresponding ending values change inthe same direction.

In certain instances, the ECG algorithm of the medical system 12.100does not include the operational rule i) and includes only one or moreof the operational rules ii), iii) and iv). This may be useful as theT-wave amplitude may be subject to certain fluctuation dependent uponthe type of the ECG lead or leads used and the individuality of thesubject.

The ECG electrodes 2812 of the medical system 2810 may include a firstset of electrodes consisting of lead II, lead V2, lead V3 and lead V4,and a second set of electrodes consisting of lead II only, wherein theECG algorithm further includes a calibration rule which is the medicalsystem is operationally calibrated if one or more of the following ismet: v) a difference between the outputs according to rule ii) from thefirst set of electrodes and the second set of electrodes is no greaterthan 20%; vi) a difference between the outputs according to rule iii)from the first set of electrodes and the second set of electrodes is nogreater than 20%; and vii) a difference between the outputs according torule iv) from the first set of electrodes and the second set ofelectrodes is no greater than 20%.

The ECG electrodes 2812 of the medical system 2810 may include a firstset of electrodes consisting of lead II, lead V2, lead V3 and lead V4,and a second set of electrodes consisting of lead II only, wherein theECG algorithm further includes a calibration rule which is the medicalsystem is operationally calibrated if one or more of the following ismet: v) a difference between the outputs according to rule ii) from thefirst set of electrodes and the second set of electrodes is no greaterthan 10%; vi) a difference between the outputs according to rule iii)from the first set of electrodes and the second set of electrodes is nogreater than 10%; and vii) a difference between the outputs according torule iv) from the first set of electrodes and the second set ofelectrodes is no greater than 10%.

Referring back to FIG. 28, the medical system 2810 may further include adialysis device 2813 such that the subject 2820 is subject to serumpotassium concentration monitoring via the use of the electrocardiogramelectrodes and the ECG algorithm while being subject to a treatment bythe dialysis device. This design provides a real-time feedback controlof the dialysis operation based upon the output on the serum potassiumconcentration. If the serum potassium levels are out of normal ranges,this may be detectable via changes in the ECG. Subjects with end-stagerenal disease who are on dialysis have large fluctuations in systemicpotassium levels between dialysis sessions. They may be hypokalemic atthe end of a dialysis session, and as their potassium levels risebetween dialysis sessions, they may become hyperkalemic before the nextsession. Hemodialysis subjects have a high rate of sudden death. Theirpotassium fluctuations could lead to cardiac arrhythmias. Both hyper-and hypokalemia are well-known risk factors for sudden cardiac death.

A non-limiting example of a method of monitoring the ECG features andestimating the potassium concentration is provided as follows. A signalfrom an implanted device is recorded periodically and stored in thedevice. At specified times, either at the same time each day, or beforeand after dialysis, stored segments of the signals are uplinked to aprogrammer device. The segment of data is preprocessed by being high-and low-pass filtered and possibly inverted. The least noisy sections ofdata may be identified to be used for measurement. An R-wave is detectedas the largest amplitude peak within the first few seconds of a datasegment. After a blanking period, additional R-waves are identifiedwhich are of comparable polarity, magnitude, and QRS width. The R-wavesfrom a segment of data, nominally 1 minute, may be used to construct amedian beat representing the signal. The R-waves in the measurementwindow following the selected template time are detected by theamplitude peaks. They are subsequently sorted by R-R interval, and thelongest 1/12th and shortest 1/12th of intervals are thrown out. Theremaining complexes are time-scaled to the average R-R, and then themedian value for each sample in the R-R interval is selected to form themedian beat. ECG features are measured either on every beat, asidentified by an R-wave, or just on the median beat. ECG featuresincluding some of the following are identified: T-wave amplitude, R-waveamplitude, T-wave amplitude/R-wave amplitude ratio, T-slope, T-slopeover amplitude, T-wave peak-to-peak amplitude, R-R interval, QRSduration, and T-wave phase type. T-wave phase type is a measure ofwhether the T-wave is monophasic or biphasic. The maximum positivesignal amplitude and maximum negative signal amplitude within a windowfollowing the R-wave are identified. They are compared to the baselineamplitude following the T-wave. If either the maximum positive signalamplitude or maximum negative signal amplitude is much farther frombaseline than the other, it is a monophasic T-wave. If they areapproximately equally far from baseline, it is a biphasic T-wave. TheT-wave phase type may change from monophasic to biphasic as potassiumlevels increase. The programmer device calculates the ECG featuremeasurements from the signal which has been uplinked and compares toprevious measurements in the subject to determine if changes areoccurring, or if deviations from normal are occurring. The programmerdevice com-bines the ECG feature measurements in a weighted manner toestimate potassium concentration. This weighted sum is used as a diseaserisk score. If disease score is greater than a threshold for a period oftime, an alert is then issued.

FIG. 30 depicts an exemplified flow chart for the ECG assisted potassiumconcentration monitoring.

Referring back to FIG. 28, the medical system 2810 may further include amonitor 2818 to receive and analyze the ECG signals transmitted from theECG electrodes 2812. The monitor 2818 may be spaced apart from the ECGelectrodes 2812 and may also be co-localized with the ECG electrodes2812 to form an integral single device. Non-limiting examples of themonitor 2818 with or without being coupled with the ECG electrodesinclude Electrogram from implanted Reveal device, surfaceelectrocardiogram from external electrodes, signal from electrodes ofsubcutaneous implantable cardioverter-defibrillator, and ventricularfar-field electrogram from implantable cardioverter-defibrillator orcardiac resynchronization defibrillator. The monitor 2818 may furthercommunicate with the processor 2816 for downstream data communication,optionally via wired or wireless connection.

Those skilled in the art will readily understand that the innovationsdisclosed here can readily be applied to data and electrical signals,including ECG data, obtained from non-implantable devices. For example,a plurality of electrodes can be placed on the skin of a subject. Theplurality of electrodes can connected to a medical device for measuringelectrical signals or a patch ECG device that transmits ECG by wirelesstelemetry to a receiver that can interpret the ECG data, such as theV-PATCH™ from VPMS Asia Pacific (Victoria, Australia). Electricalsignals related to heart or lung activity and/or ECG data, regardless ofsource, can be used in conjunction with the embodiments described below.

Processing Unit and Computational Procedure

The physiological signals obtained by the med^(i)cal device of thepresent invention are processed by a processing unit. The processingunit can be computing hardware that is disposed within the implantablemedical device or external to the device, with the medical deviceillustratively including one of the medical device 410 referenced inFIG. 9, the medical device 2714 referenced in FIG. 27 and the medicaldevice 2814 referenced in FIG. 28. Alternatively, the processing unitcan be external to the patient and receive the physiological data fromthe implantable medical device and process the data either in real timeor at a later time. A computational procedure, which can be referred toas the forward computational procedure, is used to convert thephysiological signals into disease scores, which will be described belowin detail.

The processing unit can extract several details from each cardiac cycle.The complete cardiac cycle of the patient can be stored by the implantedmedical device or the processing unit and associated with a time index.In certain embodiments, not every cardiac cycle of the patient isrequired to be stored by the medical system and associated with a timeindex. For example, every other cardiac cycle or every nth integercardiac cycle can be processed. Alternatively, cardiac cycles thatoverlap certain time points can be analyzed since the time period ofcardiac cycles depends upon heart rate. In some embodiments, the timeindices of cardiac cycles indicate the chronological order of cardiaccycles, wherein adjacent time indexes are not restricted to immediatelyproximal cardiac cycles.

Table 1 lists various parameters or features that can be extracted fromthe ECG of each cardiac cycle. Each feature represents a scalar quantitythat describes a feature of the ECG of the cardiac cycles.

TABLE 1 Features extracted from the electrocardiogram Feature DefinitionF1 P-R interval F2 QRS width F3 Q-T interval or QT-dispersion F4 P-waveamplitude F5 P-wave peak F6 S-T segment depression F7 Inverted T-wavesF8 U-wave observation F9 T-wave peak amplitude F10 Heart RateVariability F11 Ratio of T-wave amplitude to R-wave amplitude F12 T-waveflatness F13 T-slope F14 T-wave peak to T-wave end time (TpkTend) F15QT/TpkTend F16 T-wave phase type

The scalar values for features F1 through F16 have diverse magnitudesand units which complicate arriving at a combination of the featuresinto one or more risk scores that can be used to assess the potassiumstate of the patient. In particular, various features are typicallyreduced to a scalar quantity in the following units: P-R interval intime units, U-wave amplitude in potential units, S2 based upon acomparison with the feature QRS width in time units, Q-T interval intime units, P-wave amplitude in potential units per time unit, P-wavepeak in potential units, and T-wave amplitude in potential units. Otherfeatures are indicated by a yes/no observations such as depression ofS-T segment and inversion of the T-wave. Therefore, each of the featuresF1 through F16 can be converted to a value on a scale from 0 to 1 toallow direct comparison and or combination of features F1 through F16,which can herein be referred to as the common scale. Those skilled inthe art will understand that scales having other ranges can be used.

Table 2 shows various computational procedures that can be used toconvert the features F1 through F16 to the common scale. Computationalprocedures D1 through D3 are discrete mathematical equations that resultin an output of either 0 or 1. As shown in FIG. 4, computationalprocedure D1 indicates a value of 1 when a determinant or thresholdX_(C) is exceeded and otherwise indicates a value of 0. Computationalprocedure D2 is similar except a value of 1 is indicated for a valueless than determinant or threshold X_(C). Computational procedure D3provides a value of 1 when the value deviates from a set point by anamount X_(C). The computational procedures S1, S2 and S3 are continuousmathematical functions with the possibility of any numerical valuebetween 0 and 1. Computational procedures D1, D2 and D3 have theadvantage of being easier to implement by a microprocessor because theyonly require a comparison of the argument X to a threshold value ofX_(C). However, the computational procedures D1, D2, and D3 do notprovide any proportional response to the input. Computational proceduresS1, S2 and S3 provide a more graded response, but impose a heaviercomputational burden on the microprocessor by either requiring amathematical computation shown in Table 2 or the use of a look-up table.However, both discrete and continuous computational procedures arecontemplated for use in the present invention. FIG. 5 presents exemplaryplots for computational procedures S1, S2 and S3.

TABLE 2 Computational procedures used for the conversion of the featuresinto s_(c)ores Name Mathematical Expression D1${D_{1}\left( {x,x_{C}} \right)} = \left\{ \begin{matrix}{1,} & {x > x_{C}} \\{0,} & {x \leq x_{C}}\end{matrix} \right.$ D2${D_{2}\left( {x,x_{C}} \right)} = \left\{ \begin{matrix}{0,} & {x > x_{C}} \\{1,} & {x \leq x_{C}}\end{matrix} \right.$ D3${D_{3}\left( {x,x_{C}} \right)} = \left\{ \begin{matrix}{1,} & {{x} > x_{C}} \\{0,} & {{x} \leq x_{C}}\end{matrix} \right.$ S1${S_{1}\left( {x,x_{C},k} \right)} = \frac{1}{1 + e^{k{({x_{C} - x})}}}$S2${S_{2}\left( {x,x_{C},k} \right)} = \frac{1}{1 + e^{k{({x - x_{C}})}}}$S3${S_{3}\left( {x,x_{C},k} \right)} = {\frac{1}{1 + e^{k{({{\frac{3}{2}x_{C}} - x})}}} + \frac{1}{1 + e^{k{({x\frac{x_{C}}{2}})}}}}$

In one embodiment, computational procedures D1 and S1 are designed toindicate that the value of a feature is increasing, where an increasedvalue is undesirable and will contribute to a disease risk scoreindicating an adverse condition. Computational procedures D2 and S2represent the reverse situation where a decreased value indicates acontribution to a disease risk score and an adverse condition.Computational procedures D3 and S3 produce high scores indicative of anadverse condition when the feature deviates from a central value eitherby increasing or by decreasing.

Below is an example illustrating the use of the features and theirconversion into raw scores using one of the discrete computationalprocedures D1 through D3. In this example, features F1 through F10 areas described in Table 1, and the value on the common scale are denotedwith P1 through P10. That is, the list below exemplifies one embodimentfor conversion of the scalar quantities for features F1 through F10 tovalue of 0 or 1 on the common scale using a computational procedureequivalent to one of D1 through D3.

If F1=P-R interval >200 msec, then P1=1, else P1=0;

If F2=QRS width >130 msec, then P2=1, else P2=0;

If F3=Q-T interval >220 msec, then P3=1, else P3=0 or if StandardDeviation of Q-T interval >20 msec, then P3=1, else P3=0;

If F4=P-wave amplitude <1 mV, then P4=1, else P4=0;

If F5=P-wave peak >1 mV/msec, then P5=1, else P5=0;

If F6=S-T segment depressed, then P6=1, else P6=0;

If F7=T-wave is inverted, then P7=1, else P7=0;

If F8=U-wave amplitude >2 mV, then P8=1, else P8=0;

If F9=T-wave peak amplitude >3 mV, then P9=1, else P9=0;

If F10=Heart Rate Variation (SDNN)<50 msec, then P10=1, else P10=0;

If F11=the ratio of T-wave amplitude to R-wave amplitude is greaterthan >0.3, then P11=1, else P11=0;

If F12=T-flatness is >0.75, then P12=1, else P12=0

If TF13=T-wave slope >15 mV/sec, then P13=1, else P13=0;

If F14=T-wave peak to T-wave end time >100 ms, then P14=1, else P14=0.

If F15=QT/TpkTend >0.25, then P15=1, then P15=0.

If F16=T-wave phase type=biphasic, P 15=1, else P15=0.

The correlation to the set of instructions described above can beexpressed using the discrete computational procedures D1, D2 or D3 tocompute the common scale values, which are shown below as P1 throughP16:

P1=D1 (F1, 200 msec);

P2=D1 (F2, 130 msec);

P3=D1 (F3, 220 msec);

P4=D2 (F4, 1 mV);

P5=D1 (F5, 1 mV/msec);

P6=D2 (F6, 1.1 mV);

P7=D2 (F7, 0);

P8=D1 (F8, 2 mV);

P9=D1 (F9, 3 mV);

P10=D2 (F10, 50 msec);

P11=D1(F11, 0.3); P12=D1(F12, 0.75);

P13=D1(F13, 15);

P14=D1(F14, 100);

P15=D1(F15, 0.25);

P16=D1(F16, 0);

Similar expressions for the raw scores P1 through P16 can be writtenusing the continuous computational procedures S1 through S3 instead ofD1 through D3. While not presented herein, the use of expressions S1 toS3 to generate common scale values being any real value between 0 and 1is readily ascertainable by one having ordinary skill in the art uponapplying a determinant X_(C) and a factor k.

Afterwards, disease scores are calculated using the raw scores. Threeexamples are given below. In this case, DSL, DSH and DAR denote thedisease scores for hypokalemic, hyperkalemic and arrhythmic outcomesrespectively. Specifically, a higher value for DSL, DSH and DARindicates an increased prevalence of the respective condition. WL1, WL5,WH2, WA1, etc. denote weighting coefficients. The weighting coefficientscan be further refined as described below. In some embodiments, theweighting coefficients can be any number greater than or equal to zero.

DSL=WL1*P1+WL6*P6+WL7*P7+WL8*P8+WL10*P10+WL11*P11+WL12*P12+WL13*P13+WL14*P14+WL15*P15+WL16*P16  (Eq.1)

DSH=WL2*P2+WL3*P3+WL4*P4+WL5*P5+WL9*P9+WL10*+WL11*P11+WL12*P12+WL13*P13+WL14*P14+WL15*P15+WL16*P16  (Eq.2)

DAR=WA1*P1+WA2*P2+ . . .+WA10*P10+WL11*P11+WL12*P12+WL13*P13+WL14*P14+WL15*P15+WL16*P16  (Eq. 3)

For the calculation of the disease scores, weighting coefficients aswell as the variables such as X_(C) and k values will need to bedetermined. For the remainder of the discussions, these variables,weighting coefficients, X_(C) and k, can be collectively denoted withthe symbol M. These constants can be predetermined and adjusted asneeded by the medical professionals attending the patient.Alternatively, the processing unit can adjust these constants based onthe patient outcomes. In some embodiments, the weighting coefficientsand value k can be set to 1, while the determinant value X_(C) is asdescribed above for each feature F1 through F16. That is, a diseasescore is calculated by a summation of individual weighed or non-weightedfeature scores as shown in Equation 4, wherein P_(k) is the featurescore and W_(k) is a weighting factor.

Risk Score=Σ_(k=1) ^(n) W _(k) *P _(k),  (Eq. 4)

The flow chart for the overall forward computational procedure thatmonitors the patient is shown in FIG. 6 and outlined in steps below:

STEP 1: Record a cardiac cycle

STEP 2: Extract features F

STEP 3: Calculate raw scores P using features F and initial variablesfrom M

STEP 4: Calculate disease scores D using raw scores P and weightingcoefficients from M

STEP 5: If disease score >threshold for a period of time, issue alert

STEP 6: Go to step 1

Disease scores can be calculated for various conditions, including butnot limited to, hypokalemia, hyperkalemia, arrhythmias, hospitalizationsand acute heart failure.

FIG. 7 shows an example trace for a disease score. In that case, thedisease score exceeds the preset threshold of 2.5 at time index T=30,but subsequently returns back to a normal zone at time index T=34. Dueto its short duration, this event does not trigger a warning. However,the disease score again enters into the risk zone at time index of T=49,and this time, it remains there for longer than 10 time indicesresulting in the issuance of a warning. The selection of the thresholdvalues as well as the minimum duration of risk can be chosen by theclinician depending on the conditions of the patient or could bedetermined by a backward computational procedure as described herein.Furthermore, the time duration before a warning is issued can bedifferent for different disease scores. For example, for hyperkalemiatime durations can be much longer than those for hypokalemia.

In certain embodiments, the controller works to identify the variablesX_(c), k as well as the weighting coefficients, and the thresholds andthe time duration before a warning is issued, which are collectivelycalled M. This is accomplished using a backward computational procedurewherein operation in the overall system is shown in FIG. 8. The featureset F is fed into the forward computational procedures as describedabove. Afterwards, the resulting disease score is compared to the actualpatient outcome. The difference, called the error signal, is used toadjust the constant set M, which is used in the future execution of theforward computational procedures. For example, if the disease score andthe patient outcome are the same, then the error signal would be zero,indicating that there is no reason to alter the constants. On the otherhand, if there is discrepancy between the disease score and the actualpatient outcomes, then the error signal would be a non-zero value, whichin turn will drive the backward computational procedure to alter theconstant set M. The backward computational procedures can be constructedusing any of the many known statistical and signal processing methodssuch as the least squares and steepest descent.

Communication System

The communication system allows the transfer of data as well as thedisease scores and the variables from set M between the implantedmedical device and the external devices for monitoring the patient 401as shown in FIG. 9. In particular, the implanted medical device orexternal medical device 410 can be in wireless communication with alocal monitor 420 located in the vicinity of the patient. The localmonitor 420 can then communicate with either a local computer that canserve as a control processor for interpreting electrical signals fromthe patient, or the electrical signals from the patient can betransmitted to a remote control processor 430. In any scenario, aclinician 440 can monitor the control processor, provide the results ofclinical observations or lab tests, or adjust the set M used in diseasescore calculation or modify thresholds or time periods for generating analert. When an alert is generated as described below, the patient 401can be made aware through a signal (e.g. audio, visual, etc.) from localmonitor 420 and a clinician monitoring the control processor 430 can bemade aware of any patient having an alert.

The implanted medical device and/or the local monitor can share andtransmit data and instructions using any known method of wired orwireless telemetry. For example, a WMTS driver in any device can providean interface for communication via protocols, such as conventional RFranges allocated by Federal Communications Commission (FCC) for WirelessMedical Telemetry Service (WMTS). A 802.11 driver in any device cansupport an 802.11 wireless communication protocol such as 802.11a,802.11b, or 802.11g. Similarly, a Bluetooth driver can support RFcommunications according to the Bluetooth protocol. Any device can alsoinclude CDMA and GSM drivers for supporting cellular communicationsaccording to the code division multiple access (CDMA) protocol, or theGlobal System for Mobile Communications (GSM) protocol, respectively.Software Applications can invoke Network Protocols to make use of thesedrivers for communication with the local monitor 420 and/or the controlprocessor 430. Network Protocols in any device can implement a TCP/IPnetwork stack, for example, to support the Internet Protocol or othercommunication protocols. The preceding is merely exemplary of methods ofcommunication that can be used by an implanted medical device 410, thelocal monitor 420 or the remote control processor 430 wherein one ofordinary skill will understand that many ways of performing theobjectives of the invention are known within the art.

Those skilled in the art will readily understand that the communicationsystem can transmit other data in addition to the specific disease scoredata disclosed herein. Rather, many other patient parameters can beobserved with sensors or inputted to evaluate the dialytic status of thepatient, which can include both the effectiveness of dialysis treatmentin replacing natural kidney function or complications due to dialysistreatment, such as undesirable changes in potassium ion levels. Datathat can be collected and transmitted by the communication systeminclude, but is not limited to, 1) Non-potassium electrolytes andbiomarkers such as sodium and calcium; 2) metabolites such as urea,glucose and lactate; 3) hemodynamic parameters such as pulmonary arterypressure, left atrial pressure, right atrial pressure, left ventricularend diastolic pressure, O₂ saturation, and cardiac output; 4) serumbiomarkers such as creatinine, albumin, beta-2-microglobin and nGAL; 5)ECG parameters and features; 6) cardiac, skeletal contraction and/orlung data obtained from accelerometer sensors; and 7) values inputted bythe patient regarding physical condition.

As will be discussed in greater detail below, ECG parameters andfeatures can be used to calculate specific risk scores. However,additional data can be used to evaluate an overall dialytic clinicalrisk score (DCRS). The DCRS can be evaluated qualitatively by aphysician or a clinician to access the overall status of the patient. Inother embodiments, a DCRS can be calculated in an automated fashionusing an algorithm and the resulting information evaluable by aphysician or a clinician, where a monitoring physician or clinician canbe made aware of patients evaluated to have a DCRS that requires furtherevaluation in an automated fashion. That is, a change in DCRS can beused to trigger an automated alert for further evaluation by a physicianor clinician. The further exploration by a physician or clinician can beassisted by the division of data components between differentialdiagnostic dashboards, wherein the physician or clinician can bedirected to a specific diagnostic dashboard that contributed to thealert, for example, hyperkalemic, hyperglycemic, hypervolemic component,etc.

In certain embodiments, the DCRS does not need to include componentsfrom all data known about the patient. Rather, the DCRS can becalculated using a skip-logic method, wherein only certain parameterscontribute to the score based upon certain criteria. For example, themeasurement of a high pulse rate may trigger the calculation of DCRSbased upon certain additional parameters such as O₂ saturation,respiration rate, blood glucose, contractile strength (as measured byaccelerometer data), and electrolytes while excluding other parameters.As such, the basis for a DCRS score can change based upon specificpatient data. Still further, in certain embodiments ECG data and/orheart contractile strength data can provide an indication of sodium ionconcentration in the blood serum or in extracellular fluids.

As discussed above, FIG. 9 shows a communication system in accordancewith some embodiments where an implanted or external medical device 410can be in wireless communication with a local monitor 420 located in thevicinity of the patient that can relay data from the medical device 410to a remote process 430 and/or a clinician 440. FIG. 10 presentsadditional embodiments for the communication of data and otherinformation from and to a medical device including medical devices formonitoring an ECG or other electrical signals, including internal orexternal medical devices. The medical device can also include sensors orother medical devices for measuring any patient parameter including theparameters discussed above such as electrolytes, hemodynamic parameters,serum biomarkers, cardiac or skeletal muscle response and respiration,or patient-reported information.

In FIG. 10, a medical device 1000, which can be any of the medicaldevices or sensors discussed above, such as the medical device 410referenced in FIG. 9, the medical device 2714 referenced in FIG. 27and/or the medical device 2814 referenced in FIG. 28, is in wirelesscommunication with an access point 1057 can be a local monitoring deviceor a Wi-Fi router or other device that provides networking capabilities.The identity of the access point 1057 is not particularly limit and caninclude any device capable of relaying data such as smart phone or aniPad® device (not shown). The medical device 1000 through the accesspoint 1057 can transmit or receive data to or from a remote device via acomputer network, pager network, cellular telecommunication network,and/or satellite communication network, or via an RF link such asBluetooth, WiFi, or MICS or as described in U.S. Pat. No. 5,683,432“Adaptive Performance-Optimizing Communication System for Communicatingwith an Implantable Medical Device” incorporated herein by reference inits entirety, wherein there is no requirement for the electroniccontroller to be implanted within the patient.

In certain embodiments, a telemetry circuit that enables programming ofthe me-dial device 1000 by means of a 2-way telemetry link. Uplinktelemetry allows device status and diagnostic/event data to be sent to aclinician or physician or another party for review to track thetreatment of a patient. Known telemetry systems suitable for use in thepractice of the present invention are contemplated by the invention.Such 2-way communication with the medical device 1000 is typically donevia a bi-directional radio-frequency telemetry link, such as theCareLink™ system (Medtronic, Inc., Minneapolis, Minn.). Further, ageneral purpose computer or any other device having computing power suchas a smart phone, iPad® or like device.

As shown in FIG. 10, in some embodiments, transmission of data to andfrom the medical device 1000 can be accomplished through a number ofdifferent external devices. Through the access device 1057, differenttypes of devices running applications for sending and receiving datafrom the medical device 1000 can be used, such as a desktop 1050 orlaptop PC 1051 or a cellular phone or smart phone device 1056. In someembodiments, data can be transmitted over the internet 1053 via a localrouter 1055 and/or modem 1054 for placement on a secure web server 1058and associated database 1059. The web server 1058 can be accessed by thepatient and/or a physician or clinician to receive or send data to themedical device 1000.

Various telemetry systems for providing the necessary communicationschannels between an electronic controller and a medical device have beendeveloped and are well known in the art, for example, telemetry systemssuitable for the present invention include U.S. Pat. No. 5,127,404,entitled “Telemetry Format for Implanted Medical Device”; U.S. Pat. No.4,374,382, entitled “Marker Channel Telemetry System for a MedicalDevice”; and U.S. Pat. No. 4,556,063 entitled “Telemetry System for aMedical Device,” which are all incorporated herein by reference. Inaddition to transmission over the internet, any device shown in FIG. 10can also directly share data with a 802.11 driver to support 802.11wireless communication protocol such as 802.11a, 802.11b, or 802.11g.Similarly, a Bluetooth driver can support RF communications according tothe Bluetooth protocol. Any device can also include CDMA and GSM driversfor supporting cellular communications according to the code divisionmultiple access (CDMA) protocol, or the Global System for MobileCommunications (GSM) protocol, respectively.

Disease Scoring

The process for calculating a disease risk score by the processor unitwill now be described with particularity. FIG. 11 presents a flowchartfor a process to monitor the real-time electrical signals of the body ofa subject that extracts values of different components from theelectrical signals including PR interval, QRS width, QT interval, P waveamplitude, P wave peak, ST segment, T waves, U wave amplitude, T wavepeak amplitude and heart rate variance corresponding to features F1through F16 as discussed above. The sequence of determining values forFeatures F1 through F16 can be different than presented in FIG. 11;however, FIG. 11 presents the order of feature determination from an ECGassociated with a particular time index in accordance with oneembodiment. In step 701, the processor unit determines the value of theP-R interval from an ECG of one cardiac cycle associated with a timeindex. In step 702, the processor unit determines the value of the QRSwidth from the ECG of one cardiac cycle associated with the time index.In step 703, the processor unit determines the value of the Q-T intervalfrom the ECG of one cardiac cycle associated with the time index. Instep 704, the processor unit determines the value of the P-waveamplitude from the ECG of one cardiac cycle associated with the timeindex. In step 705, the processor unit determines the value of theP-wave peak from the ECG of one cardiac cycle associated with the timeindex. In step 706, the processor unit determines the value of the S-Tsegment from the ECG of one cardiac cycle associated with the timeindex. In step 707, the processor unit determines T wave inversion fromthe ECG of one cardiac cycle associated with the time index. In step708, the processor unit determines the value of the U-wave amplitudefrom the ECG of one cardiac cycle associated with the time index. Instep 709, the processor unit determines the value of the T-wave peakfrom the ECG of one cardiac cycle associated with the time index. Instep 710, the processor unit determines the value of the heart ratevariance from the ECG of one cardiac cycle associated with the timeindex.

In FIG. 12, a process for transforming the values for features F1 to F16to scores on the common scale is shown. FIG. 10 shows the conversionperformed using one of the discrete computational procedures D1 throughD3 as described above. Using computational procedures D1 through D3, adeterminant X_(C) within set M must be determined. As described above,X_(C) can be set to initial values within set M or can be refined valuesas determined by application of the backwards computational procedure.In additional embodiments, the set M can include a value k to allow foruse of one of the continuous computational procedures S1 through S3, asdescribed above, for generation of one or more of the common scalevalues P1 through P10. In any scenario, the values for features F1through F16 can be used to generate values P1 though P10 on the commonscale provided that at least a determinant X_(C) is set in set M foreach of features F1 through F16. That is, each feature F1 through F16 iscompared to a determinate X_(C) for a specific feature, which can bedenoted X_(C) (F1), X_(C) (F2), X_(C) (F3), X_(C) (F4) . . . X_(C)(F16). Similarly, the value k associated with any specific feature canbe referenced by similar nomenclature: k(F1), k(F2), k(F3), k(F4) . . .X_(C) (F16).

In step 1202 in FIG. 12, the value F1 for the P-R interval is comparedto determinant X_(C) (F1) to set the value of S1 to 0 or to set thevalue of S1 to 0 using a discrete computational procedure. In someembodiments, the determinant X_(C) (F1) has a value of 200 msec. In step1205, the value F2 for QRS width is compared to determinant X_(C) (F2)to set the value of S1 to 0 or to set the value of S1 to 0 using adiscrete computational procedure. In some embodiments, the determinantX_(C) (F2) has a value of 130 msec. In step 1215, the value F3 for Q-Tinterval is compared to determinant X_(C) (F3) to set the value of S1 to0 or to set the value of S1 to 0 using a discrete computationalprocedure. In some embodiments, the determinant X_(C) (F3) has a valueof 220 msec. In step 815, the value F4 for P-wave amplitude is comparedto determinant X_(C) (F4) to set the value of S1 to 0 or to set thevalue of S1 to 0 using a discrete computational procedure. In someembodiments, the determinant X_(C) (F4) has a value of 1 mV. In step820, the value F4 for P-wave amplitude is compared to determinant X_(C)(F4) to set the value of S1 to 0 or to set the value of S1 to 0 using adiscrete computational procedure. In some embodiments, the determinantX_(C) (F4) has a value of 1 mV. In step 1220, the value F5 for P-wavepeak is compared to determinant X_(C) (F5) to set the value of S1 to 0or to set the value of S1 to 0 using a discrete computationalprocedures. In some embodiments, the determinant X_(C) (F5) has a valueof 1 mV msec⁻¹. In step 1225, the value F6 for S-T segment is comparedto determinant X_(C) (F6) to set the value of S1 to 0 or to set thevalue of S1 to 0 using a discrete computational procedure. In someembodiments, the determinant X_(C) (F6) is a yes or no determination ofwhether S-T segment is depressed. In step 1230, the value F7 for T-waveinversion is compared to determinant X_(C) (F7) to set the value of S1to 0 or to set the value of S1 to 0 using a discrete computationalprocedure. In some embodiments, the determinant X_(C) (F7) is a yes orno determination of whether the T-wave is inverted. In step 1235, thevalue F8 for U-wave amplitude is compared to determinant X_(C) (F8) toset the value of S1 to 0 or to set the value of S1 to 0 using a discretecomputational procedure. In some embodiments, the determinant X_(C)(F8)has a value of 2 mV. In step 1240, the value F9 for T-wave peak iscompared to determinant X_(C) (F9) to set the value of S1 to 0 or to setthe value of S1 to 0 using a discrete computational procedure. In someembodiments, the determinant X_(C) (F9) has a value of 1 msec. In step1245, the value F10 for heart rate variability is compared todeterminant X_(C) (F10) to set the value of S1 to 0 or to set the valueof S1 to 0 using a discrete computational procedure. In someembodiments, the determinant X_(C) (F10) has a value of 50 msec.

After the assignment of all set values, a DSL disease score iscalculated for the time index using Eq. 1 described above. In someembodiments, the weighting coefficients WL1, WL2, etc. are set to 1. Inother embodiments, the weighting coefficients WL1, WL2, etc. are set toa value found in the current set M. Similarly, a DSH disease score iscalculated for the time index using Eq. 2 described above. In someembodiments, the weighting coefficients WL1, WL2, etc. are set to 1. Inother embodiments, the weighting coefficients WL1, WL2, etc. are set toa value found in the current set M. Further a, a DAR disease score iscalculated for the time index using Eq. 3 described above. In someembodiments, the weighting coefficients WA1, WA2, etc. are set to 1. Inother embodiments, the weighting coefficients WA1, WA2, etc. are set toa value found in the current set M.

The DSL disease score calculated by Eq. 1 indicates the presence of ahypokalemia condition and the DSH disease score calculated by Eq. 2indicates the presence of a hyperkalemia condition. The presence ofhypokalemia condition and hyperkalemia condition are mutually exclusive.As such, in some embodiments the processor unit is configured to issue awarning for hypokalemia if requisite conditions are satisfied prior toissuing a warning for hyperkalemia if requisite conditions aresatisfied.

FIG. 13 shows an embodiment for determining if conditions are satisfiedfor issuing an alert for hyperkalemia or hypokalemia. In FIG. 13, awarning is issued if a DSL risk score or a DSH disease score exceeds athreshold for a set number of consecutive time indices. The thresholdfor DSL disease score or DSH disease score can be separately set and canbe refined as part of set M with the backward computational procedures.As explained above, a DSL disease score and DSH disease score are setfor each time index. The time period between adjacent time indices isknown by the processor unit. As such, a certain set of contiguous timeindices can be associated with a specific time period by the processorunit.

In step 901, the DSL disease value for a time index is compared to athreshold for DSL disease score. If the threshold is exceeded, a counterfor DSL disease score (C_DSL) is incremented by an integer value of 1.If the threshold is not exceeded, then the counter C_DSL is reset to 0.In step 905, the current count of the counter for DSL disease score(C_DSL) is compared to an alert time period which can be indicated bythe C_DSL exceeding a safe value CS_DSL. For example, if the alert timeperiod is 5 minutes and 15 seconds separate adjacent time indices, thenthe safe value CS_DSL for the counter can be set to 20, where an alertfor hypokalemia is issued in step 905 if C_DSL exceeds CS_DSL. In step910, the current count of the counter for DSH disease score (C_DSH) isincremented by an integer value 1 if the threshold for DSH disease scoreis exceeded. If the threshold for DSH disease score is not exceeded fora time index, then the counter C_DSH is reset to 0. In step 915, thecurrent count of the C_DSH counter is compared to a safe value CS_DSH.An alert for hyperkalemia is issued in step 915 if the counter C_DSHexceeds CS_DSH.

Those skilled in the art will readily understand that the steps shown inFIG. 12 represent one embodiment for determining if a DSL disease scoreand/or DSH disease score exceed a threshold value for a significantperiod to warrant that an appropriate alert be issued. Those skilled inthe art will readily recognize that whether the DSL disease score or DSHdisease score exceeds a threshold a sufficient number of times or for asufficient period of time can be evaluated by additional or alternativemeans without departing from the invention. As an example, step 901 canbe modified such that the counter for DSL disease score (C_DSL) is notreset upon evaluation of a time index that does not have a DSL diseasescore below the threshold. As an alternative, the C_DSL counter can beset to zero if a certain prior number of time indices or time indicescorresponding to a set period of time fall below the threshold for theDSL disease score. For example, the processor unit can be instructed toreset C_DSL to zero if all of the time indices from the last 3 minutes(or another appropriate time period) were below the threshold. As such,the observation of only a few time indices below the threshold will notreset the counter C_DSL nor increment the counter C_DSL by an integervalue of 1; rather, the count value of C_DSL can be left unchanged untilthe DSL disease score is observed below a threshold for an interveningperiod of time. As such, the decision to issue an alert for hypokalemiain step 905 can be based upon a moving average time frame for a numberof time indices that exceed the threshold value during a defined timewindow.

Step 910 for determining a count for C_DSH can be modified in the samemanner as for C_DSL in step 901. Further, a counter for the DAR disease(C_DAR) score exceeding a threshold can be established in the samemanner as for C_DSL and C_DSH with parallel protocols for deciding whenthe C_DAR has reached a requisite level to issue an alert forarrhythmia.

Those skilled in the art will understand that the threshold to which anyof the described risk scores are compared to for the purposes of issuingan alert, as for example as in FIG. 12, is not required to be a fixedvalue. In some embodiments, the threshold can be a fixed value, which,for example, can be correlated to specific levels of potassium ions orother electrolytes. In other embodiment, the threshold can vary and canbe recalculated during the course of monitoring of a patient. Forexample, the system can observe an average risk score for the patientover a period of time or a time window to establish a baseline riskscore value. In some instances, the baseline risk score value can beestablished during a period defined by a patient user and/or aclinician. In other instances, the baseline risk score value can bedetermined periodically by calculating an average risk score during aperiod of time where no alarms or adverse conditions are reported. Insome embodiments, the baseline risk score can be established over aperiod from 3 hours to about 2 weeks. In other embodiments, the baselinerisk score can be established over a period from about 3 hours to about1 week, from about 1 week to about 2 weeks, from about 3 days to about 2weeks, from about 3 days to about 1 week or from about 1 day to about 2weeks.

Once a baseline risk score for a patient is established, the thresholdfor any risk score described herein can be calculated based upon thebaseline risk score. As discussed above, when a risk score (e.g. DSL,DSH, DAR) exceeds a threshold for the risk score, then a counter for therespective risk score (e.g. C_DSL, C_DSH, C_DAR) advances and an alertcan be issued when the counter value exceeds a limit. The threshold towhich a risk score is compared for purposes of advancing thecorresponding counter can be a floating value that changes based uponthe determined baseline risk score. In some embodiments, the thresholdcan be set at a value that is a certain percentage greater than thebaseline risk score. In one embodiment, a threshold for a risk score canbe any of from about 10 to about 100%, from about 15 to about 50%, formabout 15 to about 40%, from about 20% to about 60% or from about 25% toabout 50% greater than the determined baseline risk score. In otherembodiments, a threshold for a risk score can be set as a specificabsolute value over the determined baseline risk score.

Since the baseline risk score for each risk score DSL, DSH and DAR canbe adjusted, a patient can be evaluated as being at risk as a result ofa relative change in risk score since the last time the baseline riskscore was calculated. As such, baseline risk scores and thresholds canaccount for patient-to-patient variability as well as gradual changes inpatient ECG parameters that do not represent a greater susceptibility tohyperkalemia/hypokalemia or arrhythmias. That is, it is possible for thebaseline risk score of patients to change overtime due to benign causesthat do not represent an increased risk for hyperkalemia/hypokalemia orarrhythmias, where such changes are gradual over time. As describedabove, the system can account for such drift in baseline risk score,where an alarm is only triggered in response to a significant increasein risk score over a relatively short period of time rather than basedupon an absolute risk score value.

In certain instances, the forward computation procedure includes onlythe electrocardiogram algorithm such that an output from the forwardcomputation procedure is an indication on the serum potassiumconcentration of the patient, at time t₁, t₂, t₃, . . . t_(n) incomparison to the baseline serum potassium concentration at time t₀.

Backward Computational Procedure

In FIG. 8, the features set to the common scale are provided in 501 foroperation on by the forward computational procedure 505. As describedabove, the forward computational procedure is any of Equations 1 through3 to calculate a disease risk score DSL, DSH and/or DAR. In step 510,periodic clinical data regarding measured patient condition can besupplied to the system. For example, information regarding serumpotassium obtained from standard laboratory tests can periodically beinputted to the control processor and compared to the disease risk scoregenerated at the time serum potassium was measured.

The threshold set for the disease risk score is correlated with anexpected potassium serum level. A discrepancy between disease risk scoreand the clinical data from step 510 can result in an error value whichis produced by the summation step (“sigma”) in step 515. When an erroris detected in step 515, the backward computational procedure can beapplied in step 520 to adjust the set of weight, determinant (X_(c))and/or k values in the set M used by the forward computational procedureto generate risk scores. The new set M can be used in the forwardcomputational procedure in step 505 going forward to refine the set M inan iterative fashion.

Each of Equations 1 through 3 is a linear combination of the product ofa weighting factor and a feature value (P) on the common scale.Refinement of determinant X_(c) and/or k value will lead to a change inthe feature value (P) that will modify the calculated disease score.Likewise, modification of the weighting factors will modify thecalculated disease score. A disease score such as DSL in Equation 1 is alinear summation of 5 product terms. Linear functions and computationalprocedures are susceptible to refinement by known statistical techniquessuch as least squares regression fit and steepest descent. Suchstatistical techniques typical require the observation of more datapoints than the number of variable to be refined for an accuraterefinement. In least square refinement, variables are brought to a stateof best fit with the number of observations by reducing the value of thesum of squares of residuals, where the residuals are the distance from abest fit value and an observed value. Here, the summation of the squaresof residuals between the calculated disease risk score calculated withrefined set M and the observed potassium serum level can be performed.

In some embodiments, the backward computational procedures to refine setM is only applied to refining one of the weighting factors, thedeterminant X_(c) or the value k. In other embodiments, each ofweighting factors, the determinants X_(c) and the values k areseparately refined to generate separate sets M. That is, for example,weighting factors are refined without modifying determinants X_(c) andthe values k; determinants X_(c) are refined without modifying weightingfactors and the values k; and the values k are refined without modifyingthe determinants X_(c) and the weighting factors. The refined set Mhaving the best fit can be maintained and carried forward to step 505.

In some embodiments, the amount of refinement can be restrained toprevent over refinement or refinement error. In some embodiments, theamount of refinement to the determinants Xc can be restrained. Forexample, the amount that determinants Xc can be modified from theirinitial values can be limited to one of about 30% or less, about 25% orless, about 20% or less, about 15% or less, about 10% or less or about5% or less. Similarly, the amount of the weighting factors can berestrained to not exceed a certain value. In some embodiments, theweighting factor can be limited to not exceed one or more from about2.5, about 2 and about 1.5.

Chronic Monitoring of Electrolytes and pH

A patient can be monitored in a chronic fashion for changes inelectrolytes in addition of potassium ion or in a manner to supplementmonitoring by ECG data only. Similarly, the patient can be monitored forchanges in pH.

One goal of hemodialysis, ultrafiltration, and like treatments is toensure that the patient's blood pH and electrolyte concentrations arewithin acceptable ranges. Typical ranges of pH and blood electrolyteconcentration that are desired during or following a blood fluid removalsession are provided in Table 3 below. As indicated in Table 3,concentrations of various acids or bases (or salts or hydrates thereof)are often important in determining the pH of blood. Accordingly, sometypical target concentrations of such acids or bases are presented inTable 3.

TABLE 3 Typical target ranges for pH and electrolytes (ref. MedicalSurgical Nursing, 7^(th) Ed., 2007) Target Range pH 7.35-7.45 Phosphate2.8-4.5 mg/dL Bicarbonate 22-26 mEq/L Cl⁻ 96-106 mEq/L Mg²⁺ 1.5-2.5mEq/L Na⁺ 135-145 mEq/L K⁺ 3.5-5.0 mEq/L Ca²⁺ 4.5-5.5 mEq/L

In hemodialysis sessions, a patient's blood is dialyzed against adialysate through an artificial dialysis membrane or using theperitoneal membrane in the case of peritoneal dialysis. The dialysatecan also serve as a replacement fluid where ultrafiltration is performedto remove fluid from the blood. Suitable components that may be used indialysate or replacement fluid include bicarbonate, acetate, lactate,citrate, amino acid and protein buffers. The concentration andcomposition of the buffers and components thereof may be adjusted basedon monitored pH of the patient's blood. Similarly, the concentration ofelectrolytes such as sodium, potassium, calcium, and chloride inreplacement fluid or dialysate may be set or altered based the monitoredlevels of electrolytes.

The methods, systems and devices described herein may be used, in someembodiments, to set the initial electrolyte concentration and pH (buffercomponents and concentration) based on monitoring that occurs before ablood fluid removal or dialysis session starts, herein referred to as ablood fluid removal session. In some embodiments, the monitoring ischronic; e.g., monitoring is performed intermittently, periodically orcontinuously over the course of days, weeks, months or years. In anattempt to minimize interference with the patient's lifestyle, themonitoring system, or components thereof, can be implantable or wearablesimilar to the devices described above.

In some embodiments, one or more sensors are employed to detect one ormore ions to gauge pH or electrolytes in the blood. In some embodiments,a sensor can have more than one transducer, even if leadless, that conmonitor more than one ionic species. By measuring more than one ionicspecies, a more detailed understanding of the levels of variouselectrolytes or blood components may be had. For example, in somepatients in some situations, one electrolyte may be at elevated levelswhile another may be at reduced levels. In some embodiments, more thanone sensor for the same ion is employed for purposes of resultconfirmation and redundancy, which can improve reliability and accuracy.In some embodiments, sensors for the same ion may be configured toaccurately detect different ranges of concentrations of the ion. Inembodiments, more than one transducer is present in a single unit. Thisallows for convenient data collection and circuitry, as all the data maybe collected in one place at the same time. Further, the multipletransducers may share the same fluid collection mechanism (e.g., amicrodialyzer in the case of an implant), and if needed or desired, mayshare the same data processing and memory storage components.

Sensor that measure pH or electrolytes by direct contact with bodilyfluids can be employed, such as ion-selective electrodes. Similarly,pacemakers or external or implantable ECG monitors (such as the Reveal®system) can be used to monitor electrolytes and can optionally be usedin conjunction with sensor that take measurements through direct contactwith bodily fluids.

Implantable sensors or sensors in which the transducer is chronicallyinserted in a tissue or blood of a patient may be calibrated prior toimplant by placement of the transducer in blood (or other conditionsmimicking the implant environment) with known pH or electrolyteconcentrations. The sensors can be recalibrated while implanted in thepatients. For example, blood pH and electrolyte concentration can bemeasured external to the patient, e.g., via blood draws, and results ofthe external monitoring can be communicated to the implanted sensor byreceiving input, e.g., from healthcare providers. Thus, the sensor, ifsensor has necessary electronics, can recalibrate based on the inputregarding the external measurements. Alternatively, or in addition, thesensor may have an internal reference built in, such as with theMedtronic, Inc. Bravo® pH sensor. Alternatively, in cases where thesensor outputs raw data to an external device, the external device maybe calibrated to interpret the raw data from the sensor with regard toinput regarding the external measurements.

Referring now to FIG. 14, the depicted method includes identifying,selecting or diagnosing a patient for which a blood fluid removal ordialysis session is indicated 800 and monitoring pH or electrolytelevels of the blood of the patient 810. The monitoring 810 can bechronic and may employ one or more implantable sensors or an ECGmonitoring device. Based on the monitored pH or electrolyteconcentration, the fluid (e.g., dialysate or replacement fluid)composition (e.g., electrolyte concentration, buffer composition andconcentration) for use initial use in a blood fluid removal session maybe set 820. As described above, the ability to chronically monitor pH orelectrolyte concentrations of the patient's blood provides the abilityto tailor the fluid composition prior to each blood fluid removalsession, as opposed to current standard practice in which the fluidcomposition is adjusted on a monthly basis (or thereabout). As multipleblood fluid removal sessions (e.g., two to three a week) may occur witha month, setting the fluid composition on a monthly basis may result inthe patient undergoing several blood fluid removal sessions with a fluidcomposition that may no longer be well suited for the patient.

Referring now to FIG. 15, method includes identifying, selecting ordiagnosing a patient for which a blood fluid removal or dialysis sessionis indicated 800 and monitoring pH or electrolyte levels of the blood ofthe patient 810. As with the method in FIG. 14, the monitoring 810 maybe chronic and may employ one or more implantable sensors or an ECGmonitoring device. The method depicted in FIG. 16 includes determiningwhether the pH or electrolyte concentration is out of range 830 based ondata acquired during the monitoring 810. For example, a determination830 can be made as to whether pH or electrolyte levels crossed athreshold (e.g., a ceiling or floor). Suitable thresholds or ranges maybe stored in, for example, a look-up table in memory of a sensor device,a blood fluid removal device, or other suitable device for purposes ofdetermining whether the pH or electrolyte concentration is out of range830 based on data acquired during the monitoring. If the pH orelectrolytes are determined to be within range, monitoring 810 maycontinue. If the pH or electrolytes are determined to be out of range(e.g., cross a threshold), an alert 840 can be issued or a blood fluidremoval session (850) may be scheduled.

The scheduled blood fluid removal session may take into account themonitored 810 pH or electrolytes, e.g. as described with regard to FIG.14. The scheduling may occur automatically, e.g. the sensor or a devicein communication with the sensor may transmit data and cause schedulingof session over internet, telephone, or other suitable network, or usingany of the communication systems described above.

Any suitable alert 840 may be issued. The alert may be a tactile cue,such as vi-bration or audible alarm, generated by a sensor or a devicein communication with sensor. The alert may provide the patient withnotice that medical attention should be sought. The alert may alsoprovide information to a healthcare provider regarding the nature of thehealth issue (e.g., pH or electrolytes out of range) and treatment(e.g., blood fluid removal session) for which the alert 840 was issued.The sensor or a device in communication with the sensor may alert thehealthcare provider by transmitting the alert or related informationover the internet, a telephone network, or other suitable network to adevice in communication with the healthcare provider.

Referring now to FIG. 16, the depicted method includes identifying,selecting or diagnosing a patient for which a blood fluid removal ordialysis session is indicated 800 and monitoring pH or electrolytelevels of the blood of the patient 810. The monitoring 810 can bechronic and may employ one or more implantable sensors or an internal orexternal ECG measuring device. Based on the monitored pH or electrolyteconcentration, the rate of flow of dialysate or blood, based in part onthe concentration of electrolytes and pH composition of the dialysate,is set 901. As described above, the rate of flow of dialysate or bloodaffects the rate of transfer of electrolytes, etc. across the dialysismembrane. Accordingly, depending on the composition of the dialysateused, the rate of flow of the dialysate or blood may be adjusted or setso that desirable blood pH and electrolyte levels may be achieved duringthe course of a treatment session.

In additionally embodiments, the one or more sensors used to monitor pHand/or electrolytes described above can be used to modify thecomposition of a dialysate or a replacement fluid during dialysis.Referring now to FIG. 17, the depicted method includes initiating ablood fluid removal or dialysis session 801 and monitoring pH orelectrolyte concentration of blood 810. As discussed above, themonitoring may occur via one or more implanted sensors or an internal orexternal ECG measuring device. Based on the monitored pH orelectrolytes, the pH or electrolyte composition or concentration offluid (e.g., dialysate or replacement fluid) used in the blood fluidremoval session may be adjusted 860. For example, based one or more ofthe current value of a monitored ionic species or the rate of change inthe monitored ionic species, the fluid composition may be adjusted, e.g.as discussed above.

Referring now to FIG. 18, the depicted method show a method where bloodelectrolyte concentration or pH is adjusted by altering the flow rate ofdialysate or blood. The method includes initiating a blood fluid removalsession 801, such as a hemodialysis session, and monitoring an indicatorof pH or electrolyte 810, which can be in the patient, upstream of thedevice, downstream of the device, within the device, or the like. Basedon the monitored data (810), adjustments to the flow of dialysate orblood may be made 900 to adjust the electrolyte concentration or pH inthe blood that gets returned to the patient.

Automated Updating of Dialysis Parameters

In certain embodiments, the monitoring of patient electrolytes or pH, asdescribed above, between dialysis treatment sessions can be used toassist in determining the appropriate scheduling or length of a futuredialysis session and/or an appropriate dialysate or replacement solutionto be used in such a session. By comparing the patient's past responsesto dialysis parameters or changes in dialysis parameters, a system canbe able to avoid future use of parameters that may harm the patient andcan learn which parameters are likely to be most effective in treatingthe patient in a blood fluid removal or dialysis session. Dialysisparameters include scheduling, length of dialysis sessions as well asdialysate or replacement fluid composition, which are referred to assystem parameters herein.

Referring to FIG. 19, a high level schematic overview of embodiments ofthe present disclosure is shown. As shown in FIG. 19, a learningalgorithm 520 is employed to determine what system parameters work wellto produce desired patient physiological results based on input. Anysuitable input variable 500 can be considered by the algorithm 520 inthe learning process. For example, variables such as how long it hasbeen since the patient's last blood removal session may be input. Suchinput could be important as patients undergoing, for example,hemodialysis on a Monday, Wednesday, Friday schedule are more likely tosuffer an adverse cardiac event just before, during or after the Mondayblood fluid removal session. Accordingly, the algorithm 520 may considerwhether a different set of system parameters should be employed when thepatient has not undergone a session in 72 hours relative to when thepatient has not undergone a session in 48 hours. Input variables 500 mayalso include whether the patient has limited time to undergo a bloodfluid removal session. The algorithm 520 can determine whether a fasterfluid removal rate should be used or whether a partial session at areduced fluid removal rate would likely be more effective based on thepatient's history of response to fast fluid removal rates.Alternatively, the patient may have additional time to undergo a bloodfluid removal session, and the algorithm 520 can take such input 500into account to determine whether there may be an advantage to slowerfluid removal rates or slower adjustment of a concentration of anelectrolyte based on the patient's history. Of course, it will beunderstood that any other suitable input variables 500 may be enteredregarding target outcomes (e.g., quick session, long session, etc.),patient history (e.g., time since last session), or the like. Inembodiments, input that takes into account future patient behavior orneeds may be entered into the system. For example, if a patient knowsthat they will miss a session or the time until their next session willbe delayed from normal, time until next session may be entered, whichmay affect the system parameters (e.g., may remove additional fluid,etc.). By way of another example, if the patient knows that they willeat or drink an amount more than optimal before the session, expectedconsumption levels may be input in the system.

As shown in FIG. 19, the algorithm 520, based on input variables 500,and patient physiological variables 510 may determine appropriate systemvariables 530 to employ based on the patient's history with blood fluidsessions under the algorithm. During a blood fluid session, systemvariables 530 may be changed and the patient physiological response maybe monitored in response to the changed system variables. If one or moreof the patient's physiological variables 510 improve, the algorithm 530can associate the changed system variables 530 with the improved patientoutcome so that the changed system variables 530 may be used later inthe session or in a future session when the patient has a similar set ofphysiological variables 510. If one or more of the patient'sphysiological variables 510 worsen, the algorithm 530 can associate thechanged system variables 530 with a worsened patient outcome so that thechanged system variables 530 may be avoided later in the session or in afuture session when the patient has a similar set of physiologicalvariables 510.

In embodiments, the input variables 500 include patient physiologicalvariables that have occurred in a time period preceding a blood fluidremoval session. For example, the time period may be a period of time(e.g., all or one or more portions of time) since the patient's lastsession. In embodiments, the input variables include input indicating(i) how long favorable patient variables 510 (e.g., above or below apredetermined threshold) were observed after the last session; (ii) therate of change of patient variables 510 following the last session,(iii) etc., all of which may be compared against system parameters 530used in the previous session. If the patient physiological 510 or othervariables (e.g. patient input regarding how the patient has felt), werefavorable since the last session, the system may employ similarvariables in future sessions. It may also or alternatively be desirableto monitor patient physiological or other variables in a time periodleading up to a session and input such variables into the algorithm 520or system before the session. The system or algorithm 520 can thendetermine whether the patient has presented with similar symptoms orparameters in previous sessions and employ system variables 530 to whichthe patient responded favorably, either in the session, after thesession, or both in the session and after the session. Accordingly, thesystem or algorithm 520 may monitor patient well-being, which may bederived from patient physiological variable 510 or input variables 500,within a session and between sessions to determine which systemvariables should be employed and changed based on the patient responseto previous sessions. As indicated by the dashed lines and arrows inFIG. 19, patient physiological variables 510 obtained between sessionsand system variables 530 used in a prior session may be input variables500 in a current or upcoming session.

In embodiments, the physiological variables 510 are monitored by sensorsthat feed data regarding the variables directly into the algorithm 520or electronics running the algorithm. The sensors may monitor fluidvolume in the patient's blood; fluid volume in the patient's tissue;concentrations of electrolytes in the patient's blood; pH of thepatient's blood; one or more cardiovascular parameter of the patient,such as blood pressure, heart rhythm, heart rate; or combinations orindicators thereof. The sensors may monitor the patient physiologicalparameters before, during or after a blood fluid removal session.

A sensor configured to monitor hemoglobin levels may also be used as anindicator of blood fluid volume, as hemoglobin concentration istypically proportional to red blood cell concentration. Thus, lower thehemoglobin concentrations may be indicative of higher blood fluidvolume. Any suitable sensor may be used to measure hemoglobinconcentration, such as sensors used in pulse oximeters which measureadsorption of red and infrared light to determine concentration ofoxygenated hemoglobin and deoxyhemoglobin, respectfully. The sensors(which may include the associated light source(s)) may be placed in anysuitable location, such as around tubing that carries blood from thepatient to the blood fluid removal device or from the blood fluidremoval device to the patient, within the blood fluid removal device, orthe like. In addition or alternatively, a sensor may be implanted in apatient and disposed about a blood vessel to measure hemoglobin levels,and thus hematocrit and blood fluid levels. By way of further example,total blood protein or albumin concentrations and blood pressure, aloneor in combination, can be used to evaluate blood volume. High bloodpressure combined with low hematocrit or low blood protein may indicatea higher possibility of blood fluid overloading. Alternatively oradditionally, blood viscosity may be used as an indicator of blood fluidvolume and may be measured by pressure or flow. Impedance, capacitance,or dialectic constant sensors may be employed to monitor fluid volume.For example, impedance may be monitored between two electrodes. Theelectrodes may be operably coupled to control and processing electronicsvia leads. The electronics are configured to generate a voltagedifferential between the electrodes, current may be measured, andimpedance calculated. The measurement may be done in either DC or ACmode. Impedance or phase angle may be correlated to tissue fluid volume.Tissue impedance sensing for purposes of monitoring tissue fluid volumehas been well documented. One example of a well studied system that maybe used or modified for use herein is Medtronic, Inc.'s OptiVol® fluidstatus monitoring system. Such a system, or other similar systems, havewell-documented procedures for determining acceptable ranges of tissueimpedance and thus fluid volume. See, e.g., (i) Siegenthalar, et al.Journal of Clinical Monitoring and Computing (2010): 24:449-451, and(ii) Wang, Am. J. Cardiology, 99(Suppl):3G-1-G, May 21, 2007.Alternatively or in addition, tissue impedance may be monitored for asuitable period of time to establish as suitable baseline, and patientmarkers or clinician input may be used to instruct whether the patientis fluid overloaded or under-loaded. The data acquired by impedancesensor and input data regarding fluid status of the patient at the timethe sensor data is acquired may be used to establish suitable ranges forimpedance values.

Suitable transducers may include an ion selective electrode configuredto detect H⁺ ions, K⁺ ions, Na⁺ ions, Ca²⁺ ions, Cl⁻ ions, phosphateions, magnesium ions, acetate ions, amino acids ions, or the like. Suchelectrodes, and components of sensors employing such electrodes, areknown in the art and may be employed, or modified to be employed, foruse in the monitoring described herein. One or more sensors may beemployed to detect one or more ions to gauge pH or electrolytes in theblood. In some embodiments, a sensor may have more than one transducer,even if leadless, that may monitor more than one ionic species. Bymeasuring more than one ionic species, a more detailed understanding ofthe levels of various electrolytes or blood components may be had. Forexample, in some patients in some situations, one electrolyte may be atelevated levels while another may be at reduced levels. In someembodiments, more than one sensor for the same ion is employed forpurposes of result confirmation and redundancy, which can improvereliability and accuracy. In some embodiments, sensors for the same ionmay be configured to accurately detect different ranges ofconcentrations of the ion. In embodiments, more than one transducer ispresent in a single unit. This allows for convenient data collection andcircuitry, as all the data may be collected in one place at the sametime. Further, the multiple transducers may share the same fluidcollection mechanism (e.g., a microdialyzer in the case of an implant),and if needed or desired, may share the same data processing and memorystorage components. A sensor (or transducer) for detecting pH,electrolyte concentration, or the like may be placed at any suitablelocation for purposes of monitoring electrolytes or pH. For example, thesensor may be implanted in the patient, located external to the patientan upstream of a blood fluid removal device, located external to thepatient and downstream of the blood fluid removal device, or the like.

One suitable implantable sensor device that is configured to monitor apatient's ECG signals is a Medtronic, Inc.'s Reveal® series insertablecardiac monitor described above. In embodiments, the sensor device maybe a suitably equipped pacemaker or defibrillator already implanted inthe patient. Monitored cardiac signals from such a device may betransmitted to a blood fluid removal device or intermediate device foruse in the blood fluid removal session or for setting the prescriptionfor the blood fluid removal session. Blood pressure monitors, which maybe external or implantable (such as Medtronic Inc.'s active leadlesspressure sensor (ALPS), which generally takes the form of a stent toanchor the device within a vessel, may be employed. Such a device may beplaced in any suitable blood vessel location, such as in a femoralartery or pulmonary artery. A wearable sensor system, such as a Holtersensor system, may be used to monitor ECG activity of the patient.Regardless of whether the sensor or sensor system employed, orcomponents thereof, is implantable, wearable, part of a largerstand-alone device, or part of a blood fluid monitoring device, thesensor may monitor any suitable cardiovascular parameter of a patient.In various embodiments, the sensors or monitoring systems are configuredto monitor one or more of heart rate, heart rhythm or a variablethereof, or blood pressure. Examples of variables of heart rhythm thatmay be measured are heart rate variability (HRV), heart rate turbulence(HRT), T-wave alternans (TWA), P-wave dispersion, T-wave dispersion, Q-Tinterval, ventricular premature depolarization (VPD), or the like.

As indicated above, sensors for monitoring patient physiologicalparameters may be, or may have components that are, implantable orwearable. In embodiments, multiple sensors may be connected viatelemetry, body bus, or the like. The connected sensors may be of thesame or different type (e.g., pH or impedance). Such connected sensorsmay be placed (e.g., internal or external) for purposes of monitoring atvarious locations of the patient's body.

Monitoring may alternatively or additionally include receiving patientor physician feedback regarding the patient's state. For example, thepatient may indicate a point in time when cramping begins, which oftenhappens when too much fluid is removed. The blood fluid monitoringdevice may include an input, such as a keyboard or touch screen displayfor entering such data. Alternatively, a separate device such as apatient programmer, laptop computer, tablet computer, personal dataassistance, smart phone or the like may be used to input the data; orthe like.

Referring now to FIG. 20, a high level flow diagram of a method isdescribed. The method includes providing input 600, such as inputvariables discussed above with regard to FIG. 20, to a blood fluidremoval system. The method also includes initiating or starting 700 ablood fluid removal or dialysis session, and learning 800 from thesession. The learning 800 may be as discussed above with regard to FIG.19 with system parameters being varied and patient physiologicalparameters being monitored to determine which system parameteradjustments result in desirable patient physiologic outcomes. Thelearning may also occur over multiple sessions by monitoring patientvariables within the sessions or by monitoring patient variables betweensessions to determine how well the patient responded prior sessions topredict how well a patient will respond to future sessions (or to setinitial parameters for future sessions based on prior experiences).

For example and with reference to FIG. 21A, additional detail regardingan embodiment of a learning process that may occur during a blood fluidremoval or dialysis session is shown. The blood fluid removal ordialysis session is started 700 and the patient is monitored 810.Monitored patient parameters, such as patient physiological variables asdiscussed above, are stored 820; e.g., in memory of the blood fluidremoval system. The system parameters, such as system variablesdescribed above, which may include rate of fluid removal from the bloodor electrolyte concentration of a dialysate or replacement fluid, areadjusted 830 and the system parameters are stored 840; e.g., in memoryof the blood fluid removal, monitoring system, or dialysis system, andpatient monitoring 810 continues. The set of stored patient parameters820 are associated 850A with a set of stored system parameters 840 sothat the system may recall particular system parameters that wereemployed at the time the patient had a given set of parameters. The dataregarding the stored patient parameters 820 and the stored systemparameters 840 may be tagged with, for example, a time event toassociate the two sets of data. Of course any other suitable method ormechanism for associating the data sets may be employed. In someembodiments, the associated data, or a portion thereof, is placed in alookup table tracking the patient's history of physiological response tochanging system parameters 860A.

Referring now to FIG. 21B, an overview of a learning process that mayoccur with monitoring between blood fluid removal or dialysis sessionsis shown. Before, during or after a blood fluid removal or dialysissession is ended 899, system parameters used in the session are stored840. The system parameters, such as system variables described above,which may include rate of fluid removal from the blood or electrolyteconcentration of a dialysate or replacement fluid, as well as anyadjustments made during the session that has just ended may be stored inmemory and associated with the patient. During one or more time periodsbetween the end of the session 899) and the start of the next session700, the patient is monitored 810. Monitored patient parameters, such aspatient physiological variables as discussed above, are stored 820;e.g., in memory of the blood fluid removal system or in memory of adevice capable of communicating with, or a part of, the blood fluidremoval system. For example, if monitoring 810, or a portion thereof,occurs via an implanted device, the implantable monitoring device may beconfigured to wirelessly communicate with a blood fluid removal systemor a device capable of communicating with the blood fluid removalsystem. If monitoring includes assays or other diagnostic procedures forwhich data is presented to a user, such as a health care provider, thedata may be entered into a blood fluid removal system or device incommunication with the blood fluid removal system. The set of storedsystem parameters 840 are associated 850B with a set of patient systemparameters 820 so that the system may recall particular systemparameters that were employed in prior sessions that resulted in a givenset of patient parameters. The data regarding the stored patientparameters 820 and the stored system parameters 840 may be tagged with,for example, a time event to associate the two sets of data. Of courseany other suitable method or mechanism for associating the data sets maybe employed. In some embodiments, the associated data, or a portionthereof, is placed in a lookup table tracking the patient's history ofphysiological response to system parameters 860B. Depending on thepatient's response (patient monitoring 810) to the prior sessions, thesystem parameters may be adjusted 83) prior to beginning the nextsession 700. The patient's responses between sessions may also affectchanges made during a session.

Referring now to FIG. 21C, an overview of a learning process thataccounts for both inter-session and intra-session patient monitoring isshown. The process depicted in FIG. 21C is mainly a composite of theprocesses depicted and described above with regard to FIGS. 21A-B. Asdepicted in FIG. 21C, the process or algorithm may include associating850A system parameters 840, and adjustments thereof 830, that result ingood or bad outcomes with regard to patient parameters 820 and mayrecall those associations for later use, e.g. in the form of a lookuptable 860A for purposes of making future adjustments to systemparameters 830 based on patient response 810 within a session. Priorpatient responses occurring between prior sessions (i.e., between end ofsession 899 and beginning of session 700) may also be taken into account(e.g., associated parameters (850B) that include patient parametersobtained between sessions) by, for example, referring to lookup table860B. If, for example, changes in systems parameters (830) within asession are associated with good (effective) or bad (ineffective)patient responses (810) between sessions, similar changes may be made oravoided, as relevant, within a session. In addition, the patientresponse (810) to a prior session or the patient's condition (810)before a session may warrant adjustment of system parameters (830) priorto beginning a session (700). The patient response (810) within priorsessions may also be taken into account (e.g., by reference to historytable 860A) in making system adjustments prior to beginning a session.

A more detailed embodiment of a within-session learning algorithm, ormethod is presented in FIG. 23A. In the embodiment depicted in FIG. 22A,a patient is monitored 810 during a blood fluid removal session. It maybe desirable to determine whether data acquired from patient monitoringis out of range 813. As used herein, “out of range” means that a valueof a monitored parameter exceeds (i.e., is above or below) apredetermined range of values. The predetermined range of values may beindicative of a patient safety concern. If the data is out of range, analert may be issued 815 or the session may be stopped 817. In somecases, it may be desirable to continue with the session, even if themonitored data, or some aspect thereof is out of range. In the depictedembodiment, if the session is continued, (e.g., due to choice or to themonitored data not being out of range), data regarding the monitoredpatient parameters is stored 820 and is compared to stored patient datapreviously obtained (e.g., in a prior session or earlier in thesession). A determination may be made as to whether the present patientparameter data is less effective 823 than stored patient parameter dataresulting from system parameter adjustments 830 that occurred just priorto the current set of system parameters. If the data is determined to beless effective 823, the stored current patient parameters 820 may beassociated 851 with stored current system parameters 840; e.g., asdiscussed above. In some cases, it may be desirable to determine whetherthe current patient parameter data, or a portion or aspect thereof, isthe least effective that has been detected in the patient in a currentor previous blood fluid removal session 825; e.g. by comparing thecurrent patient data to a history of collected patient data. If thecurrent patient data is the least effective observed 825 to date, thestored current patient parameters 820 can be associated 851 with storedcurrent system parameters 840. In this way, only the “least effective”patient conditions are tracked, as opposed to all patient conditions,which can save on memory and processing power. In any case, once thepatient and system parameter data is associated 851, the systemparameters may be adjusted 830 and the process repeated.

If the present patient parameter data is determined to not be lesseffective than stored patient parameter data resulting from systemparameter adjustments that occurred just prior to the current set ofsystem parameters, a determination may be made as to whether the presentpatient parameter data is more effective 833 than stored patientparameter data resulting from system parameter adjustments 830 thatoccurred just prior to the current set of system parameters. If the datais determined to be more effective 833, the stored current patientparameters 820 may be associated 852 with stored current systemparameters 840; e.g., as discussed above. In some cases, it may bedesirable to determine whether the current patient parameter data, or aportion or aspect thereof, is the most effective that has been detectedin the patient in a current or previous blood fluid removal session 835;e.g. by comparing the current patient data to a history of collectedpatient data (e.g., “history table” in FIG. 21). If the current patientdata is the most effective observed 835 to date, the stored currentpatient parameters 820 can be associated 852 with stored current systemparameters 840. In this way, only the “most effective” patientconditions are tracked, as opposed to all patient conditions, which cansave on memory and processing power. In any case, once the patient andsystem parameter data is associated 852, the system parameters may beadjusted 830 and the process repeated.

A more detailed embodiment of a between-session learning algorithm, ormethod is presented in FIG. 22B. In the embodiment depicted in FIG. 22B,patient is monitored 810 between a blood fluid removal or dialysissessions. It may be desirable to determine whether data acquired frompatient monitoring 810 is out of range 813. If the data is out of range,an alert may be issued 815 prompting the patient to seek medicalattention or prompting a health care or an implanted system or device totake action. In some cases, a new session may be begun 700 if patientconditions warrant. If a new session is not initiated, the inter-sessionprocess may continue. In the depicted embodiment, if the process iscontinued, data regarding the monitored patient parameters is stored 820and is compared to stored patient data previously obtained (e.g.,between prior sessions). A determination may be made as to whether thepresent patient parameter data is less effective 823 than stored patientparameter data obtained between previous sessions. If the data isdetermined to be less effective 823, the stored current patientparameters 820 may be associated 851 with stored system parameters 840from the previous session that had ended 899. In some cases, it may bedesirable to determine whether the current patient parameter data, or aportion or aspect thereof, is the least effective that has been detectedin the patient between blood fluid removal sessions 825; e.g. bycomparing the current patient data to a history of collected patientdata. If the current patient data is the least effective observed 825 todate, the stored current patient parameters 820 can be associated 851with stored system parameters 840 from the previous session that hadended 899. In this way, only the “least effective” patient conditionsare tracked, as opposed to all patient conditions, which can save onmemory and processing power. In any case, once the patient and systemparameter data is associated 851, a recommendation as to systemparameters to be used in the next session may be made (e.g., the systemparameters for the future session can be set 830 based on the patientresponse or prior patient responses) can be adjusted 830 and the processrepeated until the next session begins 700.

If the present patient parameter data is determined to not be lesseffective than stored patient parameter data obtained from time periodsbetween prior sessions, a determination may be made as to whether thepresent patient parameter data is more effective 833 than stored patientparameter data obtained from between prior sessions. If the data isdetermined to be more effective 833, the stored current patientparameters 820 may be associated 852 with stored current parameters 840from the previous session that had ended 899. In some cases, it may bedesirable to determine whether the current patient parameter data, or aportion or aspect thereof, is the most effective that has been detectedin the patient in a time between sessions 835; e.g. by comparing thecurrent patient data to a history of collected patient data (e.g.,“history table” in FIG. 21). If the current patient data is the mosteffective observed 835 to date, the stored current patient parameters820 may be associated 852 with stored system parameters 840 from theprevious session that had ended 899. In this way, only the “mosteffective” patient conditions are tracked, as opposed to all patientconditions, which can save on memory and processing power. In any case,once the patient and system parameter data is associated 852,recommendation system parameters may set 830 based on the patientresponse or prior patient responses, and the process repeated until thenext session begins 700.

It will be understood that the processes or algorithms depicted in, anddiscussed above with regard to, FIGS. 22A-B may be combined (e.g., in amanner similar to the combination of FIGS. 21A and 21B into FIG. 21C).In this way, setting of system parameters for an upcoming session cantake into account how a patient responded to such parameters withinprior sessions, or altering of system parameters within a session maytake into account how a patient responded to such alterations betweenprior sessions.

Referring now to FIG. 23A, an embodiment of a method where more than onepatient parameter variable is evaluated in a manner similar to thatdescribed with regard to FIG. 22A. In the embodiment depicted in FIG.23A, two patient parameter variables are evaluated. However, it will beunderstood that any number of patient parameter variables may beevaluated by employing a method as depicted in FIG. 23A or using anyother suitable method. In the embodiment depicted in FIG. 23A, thevariables are labeled “primary” and “secondary”, as it may be desirableto prioritize patient parameter variables. For example, in some cases itmay be desirable to monitor blood pressure and attempt to achieve astable blood pressure at or near a target range throughout the sessionbecause hypotension is one of the most common side effects of bloodfluid removal sessions. That is, as long as other patient parameters arenot out of a predetermined range, the system may attempt to keep bloodpressure in check and make adjustments to that end. However, in somecases, reducing arrhythmias is the primary goal, as many patients forwhich a blood fluid removal process is indicated dire from complicationsdue to arrhythmias. If arrhythmias are determined to be the primarypatient parameter, the blood fluid removal system may attempt to keeparrhythmias in check and make adjustments to this effect without regardto other patient parameters, e.g., as long as the other patientparameters remain within acceptable limits.

The method depicted FIG. 23A includes monitoring patient parameters 810(at least a primary and secondary patient parameter), storing patientparameter data 820, and determining whether a parameter, or aspectthereof, is out of a predetermined range 813. If the parameter is out ofrange, an alert may be issued 815, the blood fluid removal session maybe stopped 817 or the session may continue. If the parameters aredetermined to not be out of range 813, the system parameters may beadjusted 843 and stored 840. A determination may then be made as towhether the primary patient parameter is less effective 843, e.g. bycomparing current patient parameter data to stored patient parameterdata resulting from system parameter adjustments that occurred justprior to the current set of system parameters. If the primary patientparameter is determined to be less effective 843, the current storedpatient parameter data may be associated 853 with the current storedsystem parameters. Alternatively or in addition, a determination may bemade as to whether the current patient parameter data regarding theprimary parameter is the least effective that has been detected in thepatient in a current or previous blood fluid removal session 845; e.g.,as discussed above with regard to FIG. 22A. If it is the leasteffective, the current stored patient parameter data may be associated853 with the current stored system parameters as described above withregard to FIG. 22A. Similarly determinations as to whether the primarypatent parameter data is more effective 853 or the most effective todate 855 can be made and stored system and patient parameters may beassociated 854. Similar determinations regarding whether the secondarypatient parameter, or a value associated therewith, is less effective863, the least effective 865, more effective 873, the most effective 875and appropriate associations 855, 856 can be made. In this manner, thesystem may identify and learn how system parameters may affectindividually monitored patient parameters, such as blood pressure, heartrate, fluid volume, and electrolyte concentration. Based on thisinformation, the system may make choices as to which system parametersmay be employed to produce results that are likely to be favorable tothe patient.

Referring now to FIG. 23B, an embodiment of a method where more than onepatient parameter variable is evaluated between blood fluid removal ordialysis sessions in a manner similar to that described with regard toFIG. 22B. In the embodiment depicted in FIG. 23B, two patient parametervariables are evaluated. However, it will be understood that any numberof patient parameter variables may be evaluated by employing a method asdepicted in FIG. 23B or using any other suitable method. In theembodiment depicted in FIG. 23B, the variables are labeled “1º” and“2º”. However, such labeling does not necessarily imply that onevariable is more important than another. While one variable may, in somecircumstances be considered more important, the labeling of “primary”and “secondary” may merely imply that the variables being monitored andtracked are different from one another.

The method depicted FIG. 23B includes ending a blood fluid removalsession 899 and storing system parameters 840 from the ended session,which may be done during the session or after the session has ended (asdepicted). The method also includes monitoring patient parameters 810(at least a primary and secondary patient parameter), storing patientparameter data 820, and determining whether a parameter, or aspectthereof, is out of a predetermined range 813. If the parameter is out ofrange, an alert may be issued 815, prompting the patient to seek medicalattention or prompting a healthcare provider or system or device to takeaction. In some cases, a blood fluid removal process can be initiated700, e.g. if warranted or desired. If the parameters are determined tonot be out of range 813 or if a blood fluid session is not initiated, adetermination may be made as to whether the primary patient parameter isless effective 843, e.g. by comparing current patient parameter data tostored patient parameter data resulting from system parameters used inthe previous session. If the primary patient parameter is determined tobe less effective 843, the current stored patient parameter data may beassociated 853 with the stored system parameters from the previoussession. Alternatively or in addition, a determination may be made as towhether the current patient parameter data regarding the primaryparameter is the least effective that has been detected in the patientbetween blood fluid removal sessions 845; e.g., as discussed above withregard to FIG. 22B. If it is the least effective, the current storedpatient parameter data can be associated 853 with the stored systemparameters as described above with regard to FIG. 22B. Similarlydeterminations as to whether the primary patent parameter data is moreeffective 853 or the most effective to date 855 can be made and storedsystem and patient parameters may be associated 854. Similardeterminations regarding whether the secondary patient parameter, or avalue associated therewith, is less effective 863, the least effective865, more effective 873, the most effective 875 and appropriateassociations 855, 856 can be made. In this manner, the system mayidentify and learn how system parameters employed in previous sessionsmay affect individually monitored patient parameters, such as bloodpressure, heart rate, fluid volume, and electrolyte concentration. Basedon this information, the system may make choices as to which systemparameters may be employed in future sessions to produce results thatare likely to be favorable to the patient.

As depicted in FIG. 23B, recommended system parameters may be set 830based on how the patient responded to the prior session or the patient'scondition prior to the upcoming session. The recommended systemparameters may be adjusted or set 830 more than once during the processof monitoring the patient between sessions or at the end of theinter-session monitoring before initiating the next blood fluid removalsession 700.

It will be understood that the processes or algorithms depicted in, anddiscussed above with regard to, FIGS. 23A-B may be combined (e.g., in amanner similar to the combination of FIGS. 21A and 21B into FIG. 21C).In this way, setting of system parameters for an upcoming session maytake into account how a patient responded to such parameters withinprior sessions, or altering of system parameters within a session maytake into account how a patient responded to such alterations betweenprior sessions.

Referring now to FIG. 24A, a flow diagram depicting a process where thecombined response of two or more patient parameters to changes in systemparameters 830 is tracked within a session. For the purposes ofconvenience some of the steps depicted and described above with regardto FIGS. 22A and 23A are omitted from FIG. 24A. However, it will beunderstood that the same or similar steps may be employed with regard tothe method depicted in FIG. 24A. In the depicted embodiment, patientparameters and system parameters are stored 857, 858 only when both theprimary and secondary patient parameters are determined to become lesseffective 843, 863 or more effective 853, 873. In this manner, thesystem may identify or learn which system parameters result in desirable(or undesirable) changes in multiple patient parameters.

Referring now to FIG. 24B, a flow diagram depicting a process where thecombined response of two or more patient parameters to changes in systemparameters 830 is tracked between sessions. For the purposes ofconvenience some of the steps depicted and described above with regardto FIGS. 22B and 23B are omitted from FIG. 25B. However, it will beunderstood that the same or similar steps may be employed with regard tothe method depicted in FIG. 24B. In the depicted embodiment, patientparameters are stored 857, 858 only when both the primary and secondarypatient parameters are determined to become less effective 843, 863 ormore effective 853, 873 and can be associated with stored systemparameters 840 for the previously ended session 899. In this manner, thesystem may identify or learn which system parameters result in desirable(or undesirable) changes in multiple patient parameters.

Through the association of patient parameter data and system parameterdata as shown in FIGS. 21-24 and discussed above, a history of patientresponses, within sessions or between sessions, to changing systemparameters may be obtained. This history, which may be in the form ofone or more lookup table, may be consulted prior to or during a bloodfluid removal session to determine which system parameters, given thepatient's physiological parameters at a given point in time, are morelikely to cause the patient to respond favorably and which systemparameters are more likely to cause the patient to respond negatively.Accordingly, the system may respond by adjusting or setting parametersto those that are more likely to cause the patient to respond favorably.

For example and with reference to FIG. 25, a flow diagram is shown thatdepicts and embodiment of how stored and associated data (e.g., asdiscussed above with regard to FIGS. 21-24) can be used to determinewhich system parameters to use at a given time in or before a bloodfluid removal session. The method includes monitoring patient parameters810, within a blood fluid removal session or between sessions, andconsulting history lookup table 880, which may be generated byassociating system parameters and patient parameters as described abovewith regard to FIGS. 21-24. Monitoring the patient 810 may includemonitoring physiological variables or receiving input from the patient,a healthcare provider, or the like. A value associated with the currentpatient parameter data (obtained from monitoring 810) is compared todata regarding a corresponding value in the lookup table, and adetermination is made as to whether the current patient parameter issimilar to prior patient parameter data stored in the history table 882.By way of example, a value of a current patient parameter data set maybe determined to be similar to a corresponding value in the lookup tableif the values are within 10%. The system may consult the lookup table toidentify the closest corresponding value, if more than one correspondingvalue is within the predetermined cutoff for being considered similar(e.g., within 10%). As used herein, a “corresponding” value is a valueof the same parameter obtained at different times. The value may be amagnitude, a rate of change, an average, or the like. The parameter maybe blood pressure, heart rate, fluid volume, concentration ofelectrolyte, or the like.

If more than one parameter or value of a parameter is compared to datain the lookup table, the system may determine whether each value foreach parameter is within the predetermined cutoff for being consideredsimilar and identify a prior patient parameter data set as being mostsimilar by prioritizing or weighting parameters or by summing thepercent differences between all of the current values and thecorresponding values in the lookup table. Regardless of how the systemdetermines whether a current patient parameter data set is similar, ormost similar, to a prior patient data set stored in the history table, adetermination may be made as to whether the patient's response to thesystem parameters associated with the stored patient parameter datatable was a favorable response 884; e.g., was “more effective” or “mosteffective” as discussed above with regard to FIGS. 22-24. If the priorpatient response was determined to be a good response, the systemparameters may be set or adjusted according to the parameters stored inthe lookup table 892. If the prior patient response was considered tonot to be similar 882 or good 884, a default table may be consulted 888which contains non-patient specific system parameters that wouldgenerally be considered suitable in general circumstances or that wouldbe considered suitable for a patient presenting with the currentphysiological parameters. The system parameters may then be set oradjusted according to the parameters stored in the default table 890.

It will be understood that prior patient negative responses (e.g., “lesseffective”, “least effective to date”) may be stored in a lookup table,accessed and used in a similar manner to that described with regard tothe “good” responses in FIG. 25. In some embodiments, separate lookuptables are maintained for “more effective” responses (e.g., an“increased effectiveness” data table) and for “less effective responses”(e.g., a “decreased effectiveness” data table). In some embodiments, the“increased effectiveness” lookup table and the “decreased effectiveness”lookup table are the same data table, which stores patient parametersand associated system parameters that resulted in “more effective”,“most effective”, “less effective” or “least effective” patientparameters. As discussed above, lookup tables may include informationregarding patient data obtained within a session or between sessions.

For purposes of example and to provide some clarity with regard to howone (or a blood fluid removal or dialysis system or monitoring system)can determine whether patient parameter data is “out of range”, “moreeffective”, “less effective”, and the like (e.g., as discussed abovewith regard to FIGS. 22-24), graphical schematic data is presented inFIG. 26 showing representations of monitored data (not actual data) forblood pressure (BP), heart rate (HR), and potassium concentration in thepatient's blood ([K⁺]). In the schematic illustration, a blood fluidremoval session is initiated at T1 and is ended at T4. System parametersare changed at times T2 and T3. The patient parameters (BP, HR, [K⁺])are shown as changing in response to the changes in blood fluid removalsystem parameters and continuing to change after the session ends. Asshown, not all patient parameters will respond similarly (e.g., moreeffective or less effective) in response to a system parameter change orsession. In the depicted schematic illustrations, a desired target valueis shown for each patient parameter. If the monitored data valueachieves or approaches the target, a determination may be made that thechange in system parameter or an overall session resulted in anincreased effectiveness or “more effective” state for that parameter. Ifthe monitored data value deviates from the target, a determination maybe made that the change in system parameter or overall sessionparameters resulted in a decreased effectiveness or “less effective”state for that parameter. It will be understood that the timing of thepatient parameter response to a change in system parameters may varygreatly from patient parameter to patient parameter. In some cases,changes in a patient parameter may be observed within seconds or minutesof a change in a system parameter. In other cases, a change in a patientparameter in response to a change in a system parameter may take hoursor more to be fully appreciated or observed.

In the graphical depictions of the represented monitored data presentedin FIG. 27, a lower threshold value and an upper threshold value aredepicted by horizontal dashed lines. If the monitored data for a patientparameter exceeds the upper threshold value or crosses below the lowerthreshold value, a determination may be made that the value for thatparameter is “out of range.”

It will be understood that the condition of a patient may deterioratewith time, which is typical of patients having chronic kidney disease.Accordingly, the targets and upper and lower thresholds may vary withtime. These targets and thresholds may be modified by input from, forexample, a healthcare provider from time to time based on, e.g., thepatient's health or status of patient parameters. Alternatively, thesystem may automatically adjust target or threshold values over timebased on population data or based on data of a particular patientindicative of a generally deteriorating condition. If the target orthresholds are adjusted to or near predetermined cutoff values, an alertmay be issued to that effect.

Further, target and threshold values for one or more parameters can bemodified on a session-by-session basis. For example, if the patient isexcessively fluid overloaded prior to a given session, the target orthreshold tissue fluid levels may be adjusted upward for the next orcurrent session. The negative consequences of too much fluid removal inone session or at too fast of a rate may outweigh the negativeconsequences of higher fluid levels remaining in the patient. Additionalor more frequent fluid removal sessions may be employed to return thepatient to more desirable fluid levels.

As shown in the examples presented in FIG. 26, the patient parameterschange over time. In embodiments, values of one or more patientparameters are averaged over a period of time to account forfluctuations that may occur. The averaged value may be compared to thetarget and thresholds for determining whether a patient is improving. Byaveraging values over time, the effect of an anomalous value that maydeviate significantly from the target value or may be out of bounds maybe diminished. Of course, thresholds may be set for single occurrences,for example if the values of those occurrences may present an imminenthealth concern to the patient. In embodiments, the presence a singleoccurrence that deviates significantly from other recent occurrences mayresult in activation of a subroutine or monitoring method for detectingsimilar subsequent deviations. In embodiments, consecutive significantdeviations, a percent of significant deviations within a given number ofsamples, or the like, may result in activation or an alert or alarm.

Additional examples of systems and teachings useful in practicing theabove embodiments can be found in, for example, U.S. Provisional PatentApplication No. 61/480,532, filed on Apr. 29, 2011, now expired, andU.S. patent application Ser. No. 13/424,479 filed Mar. 20, 2012, nowPublication No. 2012/0273420A1 published on Nov. 1, 2012, both entitledELECTROLYTE AND pH MONITORING FOR FLUID REMOVAL PROCESSES, U.S. patentapplication Ser. No. 13/424,429 filed Mar. 20, 2012, now Publication No.2012/0277551A1 published on Nov. 1, 2012, entitled INTERSESSIONMONITORING FOR BLOOD FLUID REMOVAL THERAPY, and U.S. Provisional PatentApplication No. 61/480,544, filed on Apr. 29, 2011, now expired, andU.S. patent application Ser. No. 13/424,525 filed Mar. 20, 2012, nowPublication No. 2012/02777552A1 published on Nov. 1, 2012, both entitledCHRONIC pH OR ELECTROLYTE MONITORING, all which applications are herebyincorporated herein by reference in their entirety to the extent thatthey do not conflict with the present disclosure.

EXAMPLES Example 1 Objective

This example is carried out in an effort to illustrate changes inskeletal muscle potential in response to variations in serum potassiumconcentration. The changes in skeletal muscle potential are effected viaelectrical excitation externally applied to the animal. The electricalexcitation in this example is provided via a pulse stimulator coupled toan amplifier. Animal is infused with an externally applied potassiumload and serum potassium concentrations are measured from the bloodsamples taken periodically. If this can be done, the procedure may beuseful and significant in providing a method of monitoring potassiumconcentration without the subject having to go through the inconvenienceand sometimes painful experiences associated with periodic bloodsampling.

Experimental Setup

An exemplified pulsing schedule is generated for stimulating theskeletal muscle at varying rates. Briefly, the pulsing schedulegenerates a set of pulse stimulation every two seconds. Each set ofpulse consists of a train of five pulses and the each pulse within thistrain lasts only 10 milli-seconds, which is referred to as ON-time. TheOFF-time between the five pulses is altered to change the effectivefrequency of the pulse train. Stimulation starts at a frequency of f=5Hz (period=1/freq=200 milli-seconds) and the frequency of stimulation isincreased by one Hertz at each pulse, giving a series of frequencies, 5Hz, 6, Hz, 7 Hz and so on. Stimulation is turned off for two secondafter the delivery of the last pulse train at f=40 Hz. Afterwards, theprocess is repeated using an infinite loop. In this connection, FIG. 31depicts a timing diagram of the resulting pulse trains.

As depicted in FIG. 31, the timing diagram for the pulse stimulationincludes five pulses delivered starting at f=5 Hz and the frequency ofstimulation is increased by 1 Hz every two seconds, until the frequencyof stimulation has reached to f=40 Hz, while the serum potassiumconcentration is kept relatively constant.

FIG. 32 depicts an exemplified experimental setup. Briefly, a pulsegenerator in the form of a stimulator is connected to an amplifier,which in turn is connected to the muscle in the hind leg of ananesthetized canine using skin penetrating electrodes. At the same time,an optical strain gauge placed on a cantilever beam is used to measurethe bulging of the muscle as it contracts under isometric conditions.Animal is infused with 0.4 mM potassium chloride solution at a rate of100 to 200 mL/hour for 5 hours to induce hyperkalemia. Serum potassiumconcentrations are measured from the blood samples taken every 15minutes. Stimulation is applied soon after the collection of the bloodsamples to assure close correlation to measured potassium values. Theserum potassium concentration increases quite steadily as a function oftime (not shown).

Responses from the strain gauge sensor recorded (not shown) each atblood potassium concentrations of [K+]=5.6 mM and [K+]=13.2 mM,respectively. At [K+] of 5.6 mM, time (in milli-seconds) versus force(in arbitrary units) trace of the skeletal muscle while pulsestimulation containing five pulses are delivered starting at f=5 Hz tof=14 Hz. At [K+] of 13.2 mM, time (in milli-seconds) versus force(arbitrary units) trace of the skeletal muscle while pulse stimulationcontaining five pulses are delivered starting at f=5 Hz to f=14 Hz whilethe serum potassium concentration [K⁺] is at 13.2 mM. It appears thatrelatively greater responses as recorded by the strain gauge sensor areobserved with higher potassium concentration [K+] of 13.2 mM as comparedto lower potassium concentration of [K+] of 5.6 mM. In both cases, thefirst mechanical response shown during the time indices t=1 sec and t=2sec are believed to be due to the application of a pulse stimulation atf=5 Hz, the second mechanical response shown during the time indices t=3sec and t=4 sec are believed to be due to the application of a pulsestimulation at f=6 Hz., and so on.

FIG. 33 demonstrates time (in milli-seconds) versus force (arbitraryunits) trace of the skeletal muscle while pulse stimulation containingfive pulses are delivered at f=5 Hz while the serum potassiumconcentration [K⁺] is changed from 3.6 mM to 13.2 mM. In particular,FIG. 33 demonstrates that the mechanical response obtained with theapplication of the stimulation at f=5 Hz while the serum potassiumconcentrations are changed from [K+]=3.6 mM to [K+]=13.2 mM. In thatdiagram, vertical scale of each trace is the same, but the verticaloffset is increased to separate the traces for ease of visualization. Ascan be viewed from FIG. 33, the amplitude of the mechanical response ofthe skeletal muscle, as measured by the strain-gauge sensing the bulkingof the muscle, increase as the serum potassium concentration increases.

In order to quantify the mechanical response of the stimulated muscle asrecorded by the strain gauge, a number of points along the response lineare selected and are depicted in FIG. 34. In particular, FIG. 34demonstrates the selection of data points A and B for the computation ofthe response, wherein the computed response is the difference in theamplitudes of the signal at times labeled as A and B. Point A representsthe minimum of the mechanical response trace prior to the 5^(th) peak,and point B represents the maximum of the mechanical response traceduring the 5^(th) peak. Point C: Baseline value of mechanical responsetrace following the 5^(th) peak. The following two equations are used tocompute the responses:

CR1=MB−MC

CR2=MB−MA

In the equations shown immediately above, CR1 and CR2 represent thecomputed responses, and MA, MB and MC each represent the mechanicalresponse at points A, B and C.

FIG. 35 shows the computed response CR1 as a function of the serumpotassium concentration. It should be noted that the response is fairlylinear in the clinically significant range of hyperkalemia, i.e. [K+]=5mM to [K+]=9 mM. Furthermore, the anomalies in the behavior of thecomputed response CR1 at the low serum concentrations of [K+]=3 mM to[K+]=5 mM are believed to be due to experimental artifacts as the bodyinitially struggles to compensate for the sudden infusion of bolusamount of potassium, until the hyperkalemia is established.

Muscle response can be sensed using an external transducer. In thiscase, the bulging of the muscle in its mid-section during a contractioncan be detected using a pressure sensor. Such a pressure sensor may bepressed onto the muscle through the skin, or be sensing the bulging viaa secondary linkage system. In this example, a strain gauge is mountedonto a cantilever beam where the bulging of the contracted musclechanges the strain on the beam, which in turn is detected by the straingauge. Alternatively, a blood pressure cuff can be used to detect thebulging of the muscle. In this case, the blood pressure cuff is inflatedto a pressure that is sufficient to make good contact with the skin,such as 50 mm Hg. Afterwards, the pulse stimulation at the frequency off=5 Hz is applied and the resulting pressure waveform is analyzed asdescribed above. This system has the advantage of measuring the bloodpressure along with the changes in the serum potassium concentrations.

Conclusion

This example as described herein demonstrates that changes in skeletalmuscle potential can be response indicators for variations in serumpotassium concentration. This is useful and significant at least in thatpotassium concentration monitoring can be made possible without thesubject having to go through the inconvenience and sometimes painfulexperiences associated with periodic blood sampling; instead, potassiumconcentration monitoring can be conducted via a procedurally moreconvenient route such as external pressure sensor and blood pressurecuff.

Example 2 Objective

This Example is conducted to determine if changes in the serum potassiumconcentration during dialysis can be detected by changes in the featuresof the ECG (electrocardiog-raphy), using data from the PODS (PotassiumObservation in Dialysis Subjects Study). As detailed herein below,dialysis subjects are recruited and monitored in an effort to optimizetheir dialysis regimen and reduce their mortality and morbidity.Particularly various ECG features are reviewed in response to thepotassium concentration changes and the ECG features that represent themost significant and/or consistent changes are identified for thissubject population based on the data from the PODS. The data may then beused for the design of a detection algorithm for abnormality hypokalemiain heart failure subjects.

Experimental Setup

The PODS study is a single center, acute, non-randomized feasibilitystudy in which data from 23 hemodialysis subjects are obtained via anexternal DR-180+ holter monitor and AUDICOR heart sounds holter. A12-lead ECG is recorded continuously during a dialysis session using theDR-180+ recorder. Blood samples for electrolyte measurements arecollected 15 minutes prior to dialysis, at 1 hour and 3 hours after theonset of dialysis, and 15 minutes after completion of dialysis.

The ECG signal(s) analyzed from the DR-180 holter are leads II, V2, V3,and V4. The primary analysis is done using the best signal availableamongst the selected leads in order to maximize detection of P-waves,which can be very small for some subjects, and visually observable onmaybe only one lead. A duplicate run is conducted using lead II only forobtaining a direct comparison to the run conducted on the best lead.

The following ECG features are measured: P-R interval, R-wave amplitude,QRS duration, T-wave amplitude, T-wave flatness, T-wave asymmetry, QTinterval, QTc interval, TR amplitude ratio, T-slope and T-slope overamplitude.

Most of the ECG markers are evident from the diagram in FIG. 36.Amplitude measurements are made in the arbitrary units recorded in thefile. QTc is the QT interval cor-rected for heart rate.

The T-wave flatness metric is based on kurtosis which describes thepeakedness of a probability distribution. In this case, calculation offlatness characterizes the distribution of samples taken during theT-wave. The amplitude of the signal samples during the T-wave window arenormalized to obtain a unit area and flatness is calculated as aninverse function of the fourth central moment, or kurtosis.

The formulas for the central moments used are given below:

$M_{1} = {\sum\limits_{n = 0}^{N - 1}{n \cdot {V(n)}}}$$M_{k} = \left\lbrack {\sum\limits_{n = 0}^{N - 1}{\left( {n - M_{1}} \right)^{k} \cdot {V(n)}}} \right\rbrack$

As referenced in the formula shown above, M_(k) is the k′th centralmoment, V(n) is the T-wave, and n is the sample number. To find thepoint to use for Ton (start of the T-wave) given different baselinepoints, the minimum samples between the end of R-wave and the peak ofthe T-wave, and then between the peak of the T-wave and the end of theT-wave are found. The maximum of these two points is used as thebaseline for finding the area of the T-wave. The points closest to theT-wave peak, both before and afterwards, which have signal amplitudesequal to this baseline are used to define the start and end of theT-wave window.

T-wave asymmetry evaluates differences in slope and duration of theascending and descending parts of the T-wave. The time derivative of theT-wave is calculated and divided into two segments at the peak of theT-wave. Both segments are normalized with the maximum derivative withinthat segment. The descending T-wave is then flipped across the y-axisand x-axis and matched against the ascending segment. The segments arecompared sample by sample, and the asymmetry score is calculated as theresidual between the two segments. The point 50 ms after the end of theR wave is used for the start of the T-wave. Andersen, et al.preprocesses the data by calculating median beats, constructing XYZvectors (a linear combination of leads I-II and V1-V6 which creates 3orthogonal leads), and performing Principal Component Analysis tooptimize for ST-T segment information and improve stability andrepeatability of measurements. Principal Component Analysis is atechnique which converts a set of observations of possibly correlatedvariables into a set of values of linearly uncorrelated variables calledprincipal components. These preprocessing steps are not done with thisdata other than calculating median beats.

T-slope is the slope of the line drawn between the peak of the T-waveand the end of the T-wave, as shown in FIG. 37. T-slope over amplitudeis the reciprocal of the time from the peak of the T-wave to the end ofthe T-wave, as shown below.

${T\text{-}{slope}\mspace{14mu} {over}\mspace{14mu} {amplitude}} = {\frac{\frac{Amplitude}{\left( {{\,^{T}{end}} - {\,^{T}{peak}}} \right)}}{Amplitude} = \left( {{\,^{T}{end}} - {\,^{T}{peak}}} \right)}$

Plots are made to show the trend of each parameter throughout the courseof dialysis, against the potassium values. Histogram plots are also madeof the difference in each ECG parameter between the final blood draw andthe initial blood draw. The plots in the PODS study report show thepotassium axis on a scale from largest to smallest as dialysis removespotassium from the body.

Results—ECG Changes Seen on Leads II, V2, V3 and V4

The serum potassium concentrations of subjects decrease during the firsthour of dialysis for all the subjects examined in the PODS. Mostsubjects continue to experience decreases in potassium levels throughthe end of dialysis, although some experience increases after 1 hour or3 hours.

The ECG markers are measured from 60-second sections of data measuredwithin 5 minutes of the blood draw times using the “best” of leads II,V2, V3, and V4 for each subject.

The ECG features showing the most consistent changes during dialysis areT/R amplitude ratio, T-slope, T-wave amplitude, and R-wave amplitude. Ascan be observed from the following figures, and as serum potassiumconcentration decreases, the T-wave amplitude decreases in general, theR-wave amplitude increases in general, the T-wave flatness increases ingeneral, and T-slope decreases in general. More particularly, and asshown in the following Figures, the mean change in R-wave amplitude is24.2%, with a range of change of from −8.9% to 90.2%, the mean change inT-wave amplitude is 25.5% with a range of change of from −93.1% to59.7%, the mean change in T-wave flatness is 7.3% with a range of changeof from −3.0% to 38.2%, and the mean change in T-slope is −31.2% with arange of change of from −94.% to 42.5%.

The following figures including FIG. 38 to FIG. 42, open circlesrepresent data obtained prior to dialysis, filled circles represent data1 hour into dialysis, open squares represent data 3 hours into dialysis,and filled squares represent data after dialysis. These figures areplot-ted against corresponding serum potassium concentration values. TheX-axis scale on the dot plots is reversed to show progression duringdialysis from left to right as potassium concentration is marked fromgreater at the left to smaller at the right.

From the results obtained according to this Example, the following ECGfeatures (values not shown) either do not change significantly or do notchange consistently for the period of dialysis as examined: the P-Rinterval, the QRS duration, the QT interval, the QTc interval, theT-wave asymmetry, and T-slope over amplitude.

However, several ECG features do change significantly and/orconsistently for the period of dialysis as examined. For instance, andas demonstrated in FIG. 38, R-wave amplitudes increase significantlyand/or consistently during dialysis, with the majority of the examinedsubjects exhibiting a mean increase of 24.2%.

FIG. 39 demonstrates that T-wave amplitudes decrease significantlyand/or consistently during dialysis, with a mean decrease of −25.5%.FIG. 40 demonstrates that T/R wave amplitude ratios decreasesignificantly, with the majority of the examined subjects exhibitingdecreases as large as −94.8%. As the T/R amplitude ratio is the ratiobetween the T-amplitude and the R-amplitude, while exhibitingsignificant changes as reported herein, the T/R amplitude ratio is not acompletely independent parameter.

In addition, FIG. 41 demonstrates that T-wave flatness measurementsincrease significantly and/or consistently, with a mean increase of7.3%. FIG. 42 demonstrates that T-slope decrease significantly, with amean decrease of −31.2%.

Results—on the Lead II Only

The results using lead II only for all subjects are also analyzed to seewhether similar changes can be observed on the most Reveal-like vector.In this part of the Example, the best leads analysis detailed above isbelieved to provide a relatively higher likelihood of detecting theP-waves and the T-waves with dependable measures, as the leads areselected to have larger P- and T-waves with less noise. The lead II onlyanalysis is included to give an estimate of the loss of sensitivity inthe metrics of a less-than-optimal lead.

The following ECG features continue to elicit significant and/orconsistent changes during dialysis that are comparable to those elicitedin the best lead analysis: the R-wave amplitude, the T-wave amplitudeand the T/R amplitude ratio. However, the changes seen with the T-waveamplitude and the T/R amplitude referenced in the lead II only analysisare not as sub-stantially as those seen with the best lead analysis.

The other ECG markers have very similar changes using only lead II foranalysis as compared to using the best lead. For instance, the followingECG features continue to elicit insignificant and/or inconsistentchanges during dialysis for the period examined: the P-R interval, theQRS duration, the QT interval, the T-wave asymmetry, and the QTcmeasurement.

FIG. 43 demonstrates that R-wave amplitudes increase significantly, withthe majority of the examined subjects exhibiting an increase of up to66.2% in the R-wave amplitude.

FIG. 44 demonstrates that T-wave amplitudes change inconsistently and/orinsig-nificantly among the subjects examined. This suggests that theprecordial leads may exhibit relatively greater sensitivity to T-wavechanges due to potassium. In certain instances, however, changes toR-wave amplitude may be more stably maintained in response to serumpotassium concentration variation in comparison to T-wave amplitude.Without wanting to be limited to any particular theory, it is believedthat most ECG leads give a good representation of the R-wave, so changesin depolarization are likely to be seen on all vectors. R-wave amplitudemay also be more sensitive to filtering. Changes in repolarization asreflected in T-waves may be more sensitive to vector, but may be seen atlower potassium levels, so may be an earlier indicator of hyperkalemia,and better indicator of potassium abnormality. Both R-wave amplitude andT-wave metrics are of interest as markers.

FIG. 45 demonstrates that the T/R ratios decrease significantly, withthe majority of the examined subjects exhibiting a decrease of up to−91.6%. FIG. 46 demonstrates that T-slope measurements decreasesignificantly, with the majority of the examined subjects exhibiting adecrease as much as −90.8%. This appears to suggest that when lead ofdifferent sensitivity is used, at least three of the three ECG features,the T-wave amplitude, the R-wave amplitude, the T/R amplitude ratio, andthe T-slope, may each be readily used as markers or indicators for serumpotassium concentration changes.

In comparison to above-mentioned data seen with the best lead analysis,changes in T-wave flatness, T-slope over amplitude are not assubstantial. This again may suggest that these two ECG features may bevector dependent to some extent. FIG. 47 demonstrates performance of theT-wave flatness.

Repeatability Analysis

Of the subjects examined, three are enrolled twice for thisreproducibility or repeatability analysis. The following four ECGfeatures are measured and the measured values (not shown) areconsistently similar and comparable to those seen with the first sessionrun: the R-wave amplitude, the T-wave amplitude, the T/R amplitude ratioand the T-slope.

Conclusions

In the PODS described herein above, certain ECG markers are measured attimes corresponding to various blood draws throughout a dialysissession. The ECG changes during dialysis are described and quantified.The analysis is done using the “best lead” with largest P and T waves atbaseline, and repeated using only lead II, which most closely resemblesthe Reveal electrogram. The best lead is usually V4 or V2. The serumpotassium levels in the subjects range from 2.3 to 5.5 mM, so somesubjects start the dialysis session with a potassium level slightlyabove normal levels and some conclude the session at sub-normal levels,but no subject have potassium levels in a dangerous range. The ECGmetrics showing the largest and most consistent changes with change inpotassium are T/R amplitude ratio, T-slope, T-wave amplitude, and R-waveamplitude.

The particular embodiments disclosed above are illustrative only, as theinvention may be modified and practiced in different but equivalentmanners apparent to those skilled in the art having the benefit of theteachings provided herein. Furthermore, no limitations are intended withrespect to the details of construction or the design shown herein, otherthan as described in the claims below. It is therefore evident that theparticular embodiments disclose above may be altered or modified andthat all such variations are considered to be within the scope andspirit of the present invention.

1-26. (canceled)
 27. A method comprising: connecting at least oneelectrocardiogram sensor to a subject to receive one or moreelectrocardiogram features; applying an electrocardiogram algorithm tothe one or more electrocardiogram features to obtain an indicator forserum potassium concentration of the subject, wherein theelectrocardiogram algorithm includes one or more of the followingoperational rules: i) the output on the serum potassium concentrationbeing a function of the R-wave amplitude; ii) the output on the serumpotassium concentration being a function of the T-wave amplitude; iii)the output on the serum potassium concentration being a function of theT/R ratio; and iv) the output on the serum potassium concentration beinga function of the T-wave flatness; applying a forward computationalprocedure to the output to generate a risk score; and issuing an alertindicating a condition of hyperkalemia, hypokalemia or arrhythmia of thesubject based on the risk score; wherein the electrocardiogram sensorincludes one or more electrocardiogram electrodes for receiving one ormore electrocardiogram features from the subject, the one or moreelectrocardiogram features including T-wave amplitude, R-wave amplitude,T-slope, ratio of Twave amplitude to R-wave amplitude (T/R ratio), andT-wave flatness; and wherein the processor applies further comprising anelectrocardiogram algorithm for producing the output on the serumpotassium concentration in the subject based on the value of the one ormore electrocardiogram features, wherein the electrocardiogram algorithmis programmed into a computer readable medium and includes one or moreof the following operational rules: i) the output on the serum potassiumconcentration being a function of the R-wave amplitude; ii) the outputon the serum potassium concentration being a function of the Twaveamplitude; iii) the output on the serum potassium concentration being afunction of the T/R ratio; and iv) the output on the serum potassiumconcentration being a function of the T-wave flatness.
 28. The medicalsystem of claim 27, wherein the processor adjusts the forwardcomputational procedure for future use according to an error signalbased on the risk score and actual patient outcome.
 29. The medicalsystem of claim 28, wherein the processor applies the same forwardcomputational procedure in response to the error signal being zero. 30.The medical system of claim 27, wherein the processor produces an outputof the serum potassium concentration based on a value of the one or moreelectrocardiogram features.